Data Engineer jobs in Tel Aviv-Yafo

At Lusha, we're building for builders. We build fast and AI-first, so we look for builders. By a builder, we mean someone who turns "maybe" into "done". We're looking for a Senior Data Platform Engineer to join the Data Platform team, which owns the infrastructure every data team at Lusha runs on and sits between DevOps and the Data groups. You'll be the Databricks admin (Unity Catalog, compute, governance, cost), the Confluent (Kafka) and CDC admin, and own Airflow, Elasticsearch, the databases, the data S3 estate, and the Kubernetes cluster underneath. Your job is to make the platform fast, cheap, governed, and boring: if it's broken, you fix it, if it's manual, you automate it. Hands-on experience using AI coding tools daily, and building or integrating LLM and agent tooling into real workflows, is a must for this role. This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office. This might be for you if: You enjoy designing, building, and running large scale batch and streaming data pipelines in production, and owning what you ship, including when it breaks You like administering and evolving core platforms: Databricks, Confluent Kafka and CDC pipelines, Airflow, and Elasticsearch You care about reliability, performance, and cost of a shared data platform used across R&D You enjoy setting the engineering standard for data work: testing, observability, cost tagging, documentation You like driving architecture decisions across Databricks, Kafka, Elasticsearch, and AWS You actively use AI tools and build self-service tooling, including MCP servers and agent interfaces, so teams can operate the platform safely Requirements 5+ years hands-on building and operating large scale data pipelines, batch and streaming, in production Deep Spark and Databricks experience: Delta Lake, Unity Catalog, job and cluster tuning, Structured Streaming or DLT Streaming and CDC experience: Kafka (Confluent a plus), Debezium or equivalent, schema evolution and its failure modes Experience writing Airflow DAGs and operating the platform itself Expert Python and SQL, clean, tested, performant Strong data modeling and architecture judgment, with the ability to explain tradeoffs in scale, performance, and cost Infra literacy: comfortable with AWS (S3, IAM, networking basics), Terraform, Docker and Kubernetes, and CI/CD Proven experience leading cross-team technical initiatives end to end, from ambiguous ask to shipped and adopted Proven experience using AI coding tools daily, with LLM and agent tooling (MCP, embeddings, vector search) built into real workflows Nice to have: Elasticsearch at scale: indexing pipelines, cluster operations, reindex and rollout strategies FinOps or cloud cost management for data platforms Vault or similar secrets management, and Kubernetes operations beyond deploying your own service Experience inheriting a system with real debt and making it boring

We're hiring a Data Engineer to architect and own the systems that turn raw signal from merchants, customers, and experiments into something the whole company can build on. Data is at the heart of everything we do at Jurni - it powers our AI models, drives merchant-facing analytics, and informs how we make product decisions every day. This is a high-ownership role for someone who wants to shape data architecture from the ground up- not inherit someone else's roadmap. You'll be the person who makes sure our data is fast, trustworthy, and easy for every team to use. What You'll Do Architect and build scalable data pipelines processing millions of events daily from merchant stores, product usage, experiments, and third-party integrations Own our data warehouse strategy end-to-end- reliability, performance, and scalability as we grow Define our event architecture and set the standards for how the company captures and models product and business data Build data quality practices- monitoring, validation, testing, and alerting- that the rest of the team can trust Partner directly with Product and Engineering to unlock the data behind our AI features, merchant analytics, and experimentation Build self-service tooling so other teams can query and analyze data without depending on you as a bottleneck Set the bar for data modeling, orchestration, and documentation across the org What We're Looking For 5-8+ years of experience in data engineering, with a track record of owning data infrastructure end-to-end Deep experience designing and operating pipelines and warehouse architecture at scale Hands-on expertise with Postgres and modern data warehouses (we use ClickHouse) Experience with workflow orchestration for building observable, reliable pipelines (we use Temporal) Strong TypeScript skills, with the ability to ship production-grade, well-tested code Experience running services in containerized cloud environments (we use Kubernetes and AWS) Familiarity with in-memory data stores and caching (we use Redis) Sharp instincts for data modeling, event-driven architecture, and data quality A pragmatic, ownership-driven mindset- you're comfortable setting direction in ambiguous territory The ability to translate infrastructure decisions into business impact for non-technical stakeholders Nice to Have Experience building data infrastructure for AI/ML products Background in e-commerce, or platforms like Shopify Familiarity with experimentation platforms and A/B testing analytics Why Join Jurni We're a fast-moving team building the data and AI infrastructure behind the future of AI-native commerce Real ownership from day one- you'll define our data architecture and standards, not execute against someone else's plan Work closely with a small, senior team where your decisions have immediate, visible impact Competitive compensation and equity, based in our Tel Aviv office

Zafran is looking for an experienced DevOps Data Infra Engineer to join our growing team. As a Data Infra Engineer, you will play a critical role in setting up, optimizing, and maintaining our data & analytics infrastructure. You will work closely with the development and Security teams to ensure the reliability, scalability, redundancy, and security of our data infrastructure platform. The ideal candidate is passionate about big data, loves working with cutting-edge technologies, and thrives in a collaborative environment. About Zafran: Our Mission: To stop the exploitation of vulnerabilities, everywhere. What makes us different: Zafran de-risks 90% of critical vulnerabilities overnight across your hybrid environment and utilizes Agentic Capabilities and your existing security tools to rapidly mitigate and remediate the 10% most likely to be exploited. ​​Who’s behind us: Zafran is backed by Menlo Ventures, Sequoia Capital, Cyberstarts, and a deep belief that cybersecurity should move as fast as attackers do. We’re one of the fastest-growing companies in the industry, scaling to meet demand from the world’s most advanced, security-obsessed organizations. We’re serious about our mission- so expect work that matters, teammates who challenge and inspire you, and plenty of fun along the way! What you will do: • Design, implement, and manage scalable, highly available databases for both transactional, real-time use as well as backend analytics and big data. • Build, design, and maintain data processing pipeline infrastructure used by our various applications • Develop and maintain CI/CD processes for automating seamless changes to data infrastructure, including data migrations and database upgrades • Work closely with developers to make sure our data infrastructure meets existing and new feature requirements • Work alongside our Security Team to ensure our infrastructure meets rigorous security standards • Ensure the data infrastructure processes adhere to best practices in security and compliance. Requirements: • 5+ years of experience as a Data Infrastructure Engineer or in a related role (Such as DevOps with expertise in data). • Proficiency with various database systems, including: Postgres, Elasticsearch, ClickHouse, and Neo4j. • Proficiency working with Kafka. • Hands-on experience with managing databases in a self-hosted environment, preferably on Kubernetes. • Proven experience with cloud environments such as AWS, Google Cloud, or Azure. • Knowledge of containerization and orchestration tools like Docker and Kubernetes. • Experience with Infrastructure as Code tools like Terraform and CloudFormation. • Hands-on experience with monitoring and logging tools such as Prometheus, Grafana, ELK Stack, and Datadog. • Excellent problem-solving and troubleshooting skills. • Experience with disaster recovery planning and high-availability architectures • Professional certifications showing deep knowledge in Elasticsearch, ClickHouse, and relational databases are a plus.

Design, build, and maintain scalable data pipelines and infrastructure entirely within GCP Own data solutions end-to-end, from architecture and design through to production delivery Build and maintain custom connectors to advertising platforms including Google Ads, Meta (Facebook) Marketing API, and other ad platforms Design and implement data warehouse structures with a deep focus on performance, scalability and usability Continuously investigate and evaluate new GCP native tools and services, assessing their fit, building proof of concepts, and driving adoption where it makes sense Set up and configure orchestration and transformation frameworks (such as Airflow and dbt) within the GCP environment Collaborate closely with backend engineers to ensure data infrastructure aligns with product and system needs Contribute to data science initiatives and bring genuine curiosity to analytical use cases Monitor, troubleshoot, and continuously improve the reliability and performance of existing data systems Define technical standards and best practices for the data platform Build and maintain data dashboards and reports to surface insights for stakeholders, primarily using Looker Studio. Requirements 4+ years of hands-on data engineering experience Bachelor's degree in Data Engineering, Computer Science, Software Engineering or Information System Engineering - a Data Engineering degree is strongly preferred Deep expertise in data modeling and data warehouse design Proven experience in Python and common data engineering frameworks Strong hands-on experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer and related GCP data services Experience building API connectors and integrations with third-party platforms (advertising APIs such as Google Ads, Meta Marketing API, or similar are a strong advantage) Solid understanding of distributed systems, data at scale and cloud infrastructure Ownership mindset - you take responsibility, follow through and hold a high bar for quality

Tech Stack: Databricks, Python, SQL, AWS, GCP About the Role Milestone is seeking a Senior Data Engineer to design and manage the core data infrastructure that powers our analytics and AI insights. You'll go beyond traditional pipeline work — designing systems that unify data from diverse environments and enable scalable, high-quality analysis. What You'll Do Design, build, and maintain robust data pipelines and scalable architectures Work across cloud and internal systems to develop seamless data integrations Build and optimize data models supporting analytics, reporting, and ML workloads Ensure data quality, governance, and performance at scale Collaborate with backend and product teams to deliver data-driven features What We're Looking For 5+ years of data engineering experience Strong proficiency in Python, SQL, and distributed data systems (Databricks, Spark, etc.) Hands-on experience with AWS and/or GCP Ability to architect, optimize, and troubleshoot multi-environment data flows Excellent communication and collaboration skills Why Join Us? Contribute to innovative analytics products shaping developer productivity worldwide. Work with a collaborative, mission-driven team dedicated to technical excellence. Competitive salary, equity, comprehensive benefits, and career growth opportunities. Influence core architectural decisions and build infrastructure powering the future of analytics.

Huskeys is reimagining web application protection for the modern era. As applications become increasingly dynamic, multi-cloud, and AI-driven, traditional WAFs struggle to keep up. We built the first Agentic Network Security Control Plane - an AI-powered mitigation layer that works with any existing WAF. Our platform helps security teams unify protection, measure effectiveness, reduce manual overhead, and create context-aware policies that block threats without disrupting business flows. At Huskeys, we believe context is the new attack surface, and that security should empower the business - not slow it down. Security runs on data. Every decision our platform makes - every threat we catch and every business flow we keep open - depends on our ability to ingest, partition, aggregate, and trust massive volumes of log and scan data. This role owns that foundation. Why we need you? Our platform is fundamentally data-driven: we ingest huge volumes of logs, scans, and telemetry, and the value we deliver depends on how well we partition, aggregate, and cross-reference all of it. Today this work is spread thin, and we need a dedicated owner for it. You'll own the scale and performance of our data layer - designing partitioning and aggregation strategies that hold up as log volume and complexity grow, rather than ad-hoc queries that fall over. You'll be the guardian of data integrity and trust, building reliable ETLs from many different sources (cloud providers, outside scans) and the consistency guarantees that let the whole team build with confidence. And you'll set the stones for ML: we're not building models yet, but we want our data structured, clean, and accessible so we can add ML without re-plumbing everything. Without this role, our data work stays fragmented, aggregations get slower and less trustworthy as we scale, and we stay blocked from making the most of the data we already have. About the position At Huskeys, our platform ingests and cross-references massive volumes of logs, scans, and telemetry from cloud providers, CDNs/WAFs, and external scanning tools. The depth and reliability of our data layer is what makes our context-aware security decisions possible. We are looking for a Data Engineer who can independently own that data layer end-to-end - from table and schema design through partitioning, complex aggregation, and multi-source ETL - and who thinks beyond "can I write this query?" to "will this scale, is the data correct, and can the team build on it?" What Success Looks Like After 6 Months • Independent Owner: You independently own log analysis and partitioning, our complex aggregations and cross-referencing, and the strategy for scaling our data layer - making architectural calls without constant oversight. • Source of Truth: Our internal data is the best-in-class source of truth across every source we touch - cloud provider and CDN/WAF scans, log analysis, and external scanning - with strong, shared definitions for how we model, unify, and aggregate data. • Scalable Foundation: Adding new complex aggregations is easy and safe at our scale, with best practices built in, and the team has a data strategy everyone can build on. • Business Enabler: Our data layer runs with high integrity and strong SLAs, scales to onboard more clients and new use cases, and deepens our understanding of customers' environments - keeping the product growing in a scalable direction. Key Responsibilities • Table & Schema Structure - Design and own our table structures - data models, keys, types, partitioning, and relationships - and the unified models that turn many sources into one coherent, queryable shape. • Log Analysis & Partitioning - Design and own how we store and partition large-scale log data so it stays fast and queryable as volume grows. • Complex Aggregation & Cross-Referencing - Build and optimize the complex aggregations and cross-source joins that power our platform's insights and decisions. • ETLs from Multiple Sources - Build and maintain reliable ETL/ELT pipelines from many different sources - cloud providers and outside scans - landing diverse inputs cleanly into our unified tables. • Data Integrity & Observability - Ensure data consistency and correctness, and monitor pipeline health, freshness, and quality with alerting, so we meet our SLAs and catch bad data before it reaches the product. • Data Governance - Handle sensitive customer data responsibly - access control, retention, and privacy - in line with our security and compliance commitments. • Scaling Strategy - Own the strategy for scaling our data and aggregations - table design, partitioning, query performance, and cost - ahead of where we are today. • Setting the Stones for ML - Structure and prepare data (clean, feature-ready tables and pipelines) so we're ready to add machine learning when the business is. Day-to-day Reality • Types of Tasks: Primarily hands-on data engineering and system design - schema and partitioning design, building pipelines, writing and optimizing complex queries. Some meetings for planning and collaboration, but at least 70-80% is technical work. • Level of Independence: High autonomy with end-to-end ownership. When you take on the data layer, it's yours A to Z - design, implementation, monitoring, and long-term maintenance. You make architectural decisions independently with team input. • Pace: Fast-paced and dynamic. Seed-stage startup where priorities can shift; you balance building new data capabilities with keeping existing pipelines reliable. • Collaboration: A mix of independent work and cross-team collaboration. You'll work closely with backend engineers, product, and, over time, anyone building on top of our data. • Tools/Environment: Python, SQL, ClickHouse (columnar/log analysis), PostgreSQL (NeonDB), Temporal for workflow orchestration, AWS Athena, and modern observability platforms (Datadog/Grafana/ELK). We're flexible on exact tools - equivalent experience counts. The Ideal candidate • You are a pragmatic builder who balances quality and speed - you know when to ship a workable pipeline and when to invest in robust, scalable data architecture. • You are a systems thinker who designs data models, partitioning, and aggregation strategies that hold up as volume grows. • You have an ownership mindset - you take the data layer A to Z without hand-holding, thinking beyond "can I write this query?" to "will this scale, is the data correct, and can the team build on it?" • You are a collaborative engineer who enables teammates through clean, well-modeled, well-documented datasets and knowledge sharing. • You are a strong communicator who can explain data trade-offs to both engineers and non-technical stakeholders. • You thrive in startup environments with ambiguity and rapid change, and you care deeply about data integrity and correctness - not just getting a number out the door. • You're comfortable making decisions independently while seeking input when needed, and you fit the HUSKEY values: accountable, driven, adaptable, sharp, and positive. Must-Have Requirements • 5+ years of data engineering (or strongly data-focused backend) experience. • Expert-level SQL - designing schemas, modeling data, and writing and optimizing complex aggregation and cross-referencing queries. • Strong Python for building ETL/ELT pipelines and data tooling. • Large-scale data experience - hands-on with high-volume log/event data, including partitioning and performance optimization (ClickHouse, Athena, or similar columnar/analytical stores). • ETL/pipeline experience - has built and maintained reliable pipelines ingesting from multiple, diverse sources. • Data integrity & consistency - solid understanding of data quality, consistency, and reliability across distributed environments. • Scaling mindset - can design data and aggregation strategies that scale, not just work today. Note: We're flexible on specific tools. If a candidate has done equivalent work with comparable technologies, that counts - we don't require our exact stack.

We are making the future of Mobility come to life starting today. At Autofleet we support the world’s largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization. At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping. As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform—supporting real-time optimization, advanced analytics, and machine learning applications. What You'll Do • Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools. • Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets. • Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle. • Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows. • Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability. • Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions. • Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability. Requirements • 4+ years in backend data engineering or infrastructure-focused software development. • Proficient in Python, with experience building production-grade data services. • Solid understanding of SQL • Proven track record designing and operating scalable, low-latency data pipelines (batch and streaming). • Experience building and maintaining data platforms, including lakes, pipelines, and developer tooling. • Familiar with orchestration tools like Airflow, and modern CI/CD practices. • Comfortable working in cloud-native environments (AWS, GCP), including containerization (e.g., Docker, Kubernetes). • Bonus: Experience working with GCP • Bonus: Experience with data quality monitoring and alertingֿ • Bonus: Experience with Snowflake, DBT, Flink, Kafka • Bonus: Strong hands-on experience with Spark for distributed data processing at scale. • Degree in Computer Science, Engineering, or related field.

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships.. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com. Responsibilities • Provide the direction of our data architecture. Determine the right tools for the right jobs. We collaborate on the requirements and then you call the shots on what gets built. • Manage end-to-end execution of high-performance, large-scale data-driven projects, including design, implementation, and ongoing maintenance. • Optimize and monitor the team-related cloud costs. • Design and construct monitoring tools to ensure the efficiency and reliability of data processes. • Implement CI/CD for Data Workflows Requirements • 5+ Years of Experience in data engineering and big data at large scales. - Must • Extensive experience with modern data stack - Must: 1. Snowflake, Delta Lake, Iceberg, BigQuery, Redshift 2.Kafka, RabbitMQ, or similar for real-time data processing. 3.Pyspark, Databricks • Strong software development background with Python/OOP and hands-on experience in building large-scale data pipelines. - Must • Hands-on experience with Docker and Kubernetes. - Must • Expertise in ETL development, data modeling, and data warehousing best practices. • Knowledge of monitoring & observability (Datadog, Prometheus, ELK, etc) • Experience with infrastructure as code, deployment automation, and CI/CD. • Practices using tools such as Helm, ArgoCD, Terraform, GitHub Actions, and Jenkins. Our stack: Azure, GCP, Databricks, Snowflake, Airflow, Spark, Kafka, Kubernetes, Neo4J, AeroSpike, ELK, DataDog, Micro-Services, Python, SQL Your stack: Proven strong back-end software engineering skills, ability to think for yourself and challenge common assumptions, commitment to high-quality execution, and embrace collaboration

Description Kela is a defense technology startup pioneering next-generation command & control and data infrastructure systems. We empower defense organizations with robust, scalable, and secure platforms that integrate diverse sensors and AI to deliver real-time battlefield intelligence. Backed by leading investors, Kela combines Israeli innovation with global mission impact. The Platform as a Service team is Kela's customer-facing engineering function: it delivers customer projects on the Kela platform and turns what it learns into reusable products. We are looking for a Data / ML Platform Engineer to sit between our data platform and our customer-facing work. You will build the data and ML infrastructure and work directly with customer algorithm teams to drive their projects, including on-site and forward-deployed when the work needs it. You are not only a builder of internal tooling, and you are not only a customer-facing integrator. You do both, with a strong data foundation. The role is hybrid by design. It is expected to grow toward either the data platform side or the customer-facing side, based on your preference and what the team needs. You will also seed new data products from an early idea, then carry them into real customer projects. You will be a core member Kela's PaaS team, while working closely with our core software, algorithms, and DevOps teams. Responsibilities: • Support customer algorithm teams across the full ML lifecycle: data annotation, training, evaluation, and observability, covering both the platform infrastructure and the hands-on project work • Work directly with customer teams to understand their data and ML needs, unblock them, and turn what you learn into platform improvements • Seed new data products from scratch, and take them forward by deploying and adapting them in customer projects • Build and operate data and ML pipelines for multimodal sensor data (video, radar, RF, telemetry) • Set up and maintain the data environments, model onboarding flows, and evaluation workflows that customer-side research teams rely on • Partner with algorithms, product, and DevOps teams to establish data and MLOps standards across the company Requirements: • At least 3 years of experience in data engineering, MLOps, or backend engineering, building production systems • Strong proficiency in Python, with experience writing production-grade code and pipelines • Strong SQL skills and solid data modeling fundamentals • Hands-on experience with Kubernetes and containerized deployments • Experience with ML workflows in practice: training, evaluation, experiment tracking, and model deployment • Comfortable working directly with customers or technical stakeholders, and able to turn an unclear request into a working solution • Ability to work independently, and to move between building infrastructure and delivering a project • Demonstrated full-stack “builder” mentality through professional or personal projects Preferred Qualifications: • An algorithms background: able to build and run evaluations that compare models (for example, re-identification models) • Experience with a semantic layer (dbt Semantic Layer, Snowflake Semantic Views) or ontology technologies (for example, Palantir) • Experience with MLOps frameworks (for example, MLflow, Kubeflow, Airflow orchestration) • Experience with data lake architectures (Iceberg, Delta) and query engines (Snowflake, Athena, BigQuery) • Familiarity with ELT/ETL pipeline design (Airflow, dbt) • Exposure to multimodal sensor data (EO/IR, radar, RF, geospatial, IMU, LiDAR) • Background in defense, aerospace, or other mission-critical systems

The world of payment processing is rapidly evolving, and businesses are looking for loyal and strategic partners, to help them grow. Meet Nuvei, Nuvei is the global fintech building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, banking, risk, and fraud management services. Connecting businesses to their customers in more than 200 markets, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights for customers and partners to succeed locally and globally through one integration. At Nuvei, we live our core values, and we thrive on solving complex problems. We’re dedicated to continually improving our product and providing relentless customer service. We are always looking for exceptional talent to join us on the journey! Your Mission Nuvei is looking for a hands-on Data Platform Engineer to operate and evolve our enterprise Kafka ecosystem, which serves as the company’s central data streaming platform. The role is critical to our data infrastructure and supports a major ongoing migration to the cloud, while covering production operations, troubleshooting, automation, maintenance, and reliability across high-volume, low-latency, 24/7 on-premises and cloud environments. Responsibilities • Operate and monitor Kafka environments across Confluent Cloud, Confluent Platform, and Kubernetes/CFK deployments, including availability, consumer lag, replication, throughput, latency, storage, and capacity. • Troubleshoot production incidents, participate in on-call, perform root-cause analysis, and improve resilience, failover, and disaster-recovery readiness. • Manage topics, partitions, retention, ACLs, consumer groups, Kafka Connect connectors, and Schema Registry. • Deploy, configure, upgrade, patch, and scale platform components and connectors while maintaining secure access and configuration consistency. • Automate platform operations and provisioning using scripting, infrastructure-as-code, and CI/CD. • Work with DevOps, SRE, Infrastructure, Security, Architecture, Data, and application teams to onboard workloads and continuously improve platform reliability and governance. Qualifications • 2+ years of hands-on experience with Apache Kafka with strong knowledge of topics, partitions, replication, producers, consumers, and consumer groups. • Hands-on experience with Confluent Platform and/or Confluent Cloud, including Kafka Connect and Schema Registry. • Experience supporting production systems on Linux, including monitoring, logs, networking, certificates, authentication, and access control. • Scripting and automation experience using Python, Bash, PowerShell, or similar tools, plus familiarity with Git and CI/CD. • Strong ownership, troubleshooting, and communication skills, with a willingness to participate in an on-call rotation. Preferred Qualifications • Experience with Microsoft Azure, Kubernetes / Confluent for Kubernetes (CFK), Helm, or Terraform. • Experience with the Kafka Connect ecosystem, including Debezium plugins, SMTs, Replicator, and source/sink connectors. • Familiarity with observability, SRE practices, SLIs/SLOs, incident management, and disaster recovery. • Experience in payments, fintech, banking, or another regulated environment; Confluent/Kafka certification is a plus. Nuvei is an equal-opportunity employer that celebrates collaboration and innovation and is committed to developing a diverse and inclusive workplace. The team at Nuvei is comprised of a wealth of talent, skill, and ambition. We believe that employees are happiest when they’re empowered to be their true, authentic selves. So, please come as you are. We can’t wait to meet you. Benefits • Private Medical Insurance • Office and home hybrid working • Global bonus plan • Volunteering programs • Prime location office close to Tel Aviv train station Our recruitment process may use automated tools, including AI, to support application management and candidate shortlisting.

Description About Cyera Come join the company building the security operating model for the age of AI. AI has changed how data is used — and security must change with it. Cyera's mission is to empower businesses to accelerate AI adoption by defining a holistic approach to securing AI — from data to access to model. Instead of perimeter controls and static policies, Cyera provides a unified control plane that understands relationships between data, access, and behaviors across humans, systems, and AI. Backed by the world's leading investors and working with a large and growing list of Fortune 1000 companies, we are looking for world-class talent to join us as we usher in the new era of data and AI security. About the Role We're looking for a Senior Data Engineer to help build Cyera's next-generation data platform — the lakehouse foundation that will power data processing across the entire product. This is not a "write pipelines on top of someone else's platform" role, and it's not a pure infrastructure role either. It's both, deliberately. You'll own the platform end to end: the infrastructure it runs on (Spark on Kubernetes, Apache Iceberg, AWS Glue, Airflow), the frameworks and tooling that let dozens of other engineers build on it without reinventing the wheel, and the design of the data pipelines themselves. Everything you build becomes leverage for the teams around you — your abstractions, base images, CI/CD flows, and operational patterns are what make the platform usable at scale. You'll also own one of the hardest ongoing trade-offs in a high-scale data platform: balancing cost and performance. Compute sizing, storage layout, partitioning and compaction strategy, job scheduling — every decision has a price tag and a latency profile, and you'll be the one making those calls with data. This role is ideal for an engineer who is equally comfortable debugging a Spark executor OOM on Kubernetes at 10am, designing a clean Python framework API at noon, and modeling the cost impact of a table layout change in the afternoon. What You'll Do Platform & Infrastructure - Design, deploy, and operate our Spark-on-Kubernetes compute platform, including autoscaling, resource tuning, and multi-tenancy considerations. - Own the lakehouse storage layer built on Apache Iceberg and AWS Glue catalog — table design, partitioning, compaction, schema evolution, and retention. - Build and operate orchestration on Airflow: DAG standards, deployment flows, environment promotion, and reliability. - Own production operations of the platform: monitoring, alerting, incident response, and continuous hardening. Frameworks & Developer Enablement - Build the code frameworks, libraries, and templates that other engineers use to write pipelines — so that spinning up a new production-grade Spark job is measured in hours, not weeks. - Define and enforce standards for pipeline structure, testing, observability, and deployment across teams. - Own CI/CD for data workloads: image builds, artifact promotion, and GitOps-based delivery. - Act as a technical partner to product and research teams building on the platform — your customers are other engineers. Data Pipelines & Architecture - Design and build scalable batch and streaming pipelines processing complex, high-volume datasets from diverse sources. - Lead large-scale backfills and migration initiatives, ensuring data consistency and integrity across evolving storage and compute platforms. - Design event-driven data flows over large-scale queue systems (Kafka) for reliable, efficient data movement. Cost & Performance - Continuously balance cost against performance: right-size compute, tune queries and jobs, optimize storage layout and file sizes, and choose the correct engine for each workload. - Build cost visibility and attribution into the platform so trade-offs are made with data, not guesswork. What You Bring - 5+ years of experience in software engineering, with meaningful time spent building and operating large-scale data platforms. - Strong hands-on experience with distributed processing engines (Spark strongly preferred), including performance tuning and debugging in production. - Practical experience deploying and operating workloads in Kubernetes-based environments — you're not afraid of infra work; you enjoy it. - Experience building shared frameworks, libraries, or internal tooling used by other engineers, with the product mindset that comes with it (clean APIs, docs, versioning, backward compatibility). - Strong proficiency in SQL and data modeling: complex analytical queries, query tuning, partitioning strategies. - Solid software engineering fundamentals in Python (and/or Scala/Java): testing, code review culture, CI/CD. - Experience with cloud-native data infrastructure on AWS (or equivalent) at high scale. - A strong sense of ownership — from design through deployment, operation, and cost. - Ability to thrive in a fast-changing environment, making pragmatic decisions with incomplete information. Nice to Have - Hands-on experience with Apache Iceberg or other open table formats (Delta Lake, Hudi) — compaction, schema evolution, catalog management. - Experience with EMR on EKS, Spark Operator, or similar Spark-on-Kubernetes setups. - Experience with Airflow at scale (custom operators, deferrable operators, multi-environment deployment). - Kafka-heavy or event-driven architectures; CDC pipelines (Debezium or similar). - GitOps tooling and infrastructure-as-code. - Experience with FinOps / cloud cost optimization for data workloads. - Background in cybersecurity-related data infrastructure or compliance-constrained environments (e.g., FedRAMP / GovCloud). - Contributions to open-source data engineering tools or frameworks. Why Join Us? At Cyera, we own what we build and how we work. Cyerans are empowered to take initiative, move quickly, and turn ideas into impact. We push boundaries by challenging the status quo, learning fast, and continuously raising the bar — for ourselves and for the industry. We elevate together by lifting each other up, celebrating wins as a team, and recognizing that our success is shared. In this role specifically: everything you build becomes the foundation other engineers stand on. You'll work at the technical heart of the company's data platform, solve problems that only exist at real scale, and shape how an entire engineering organization works with data. Feel free to apply even if your experience doesn't tick every box. We're building something special here — and we welcome Cyerans with diverse backgrounds, perspectives, and experiences.

Zafran is looking for an experienced DevOps Data Infra Engineer to join our growing team. As a Data Infra Engineer, you will play a critical role in setting up, optimizing, and maintaining our data & analytics infrastructure. You will work closely with the development and Security teams to ensure the reliability, scalability, redundancy, and security of our data infrastructure platform. The ideal candidate is passionate about big data, loves working with cutting-edge technologies, and thrives in a collaborative environment. About Zafran: Our Mission: To stop the exploitation of vulnerabilities, everywhere. What makes us different: Zafran de-risks 90% of critical vulnerabilities overnight across your hybrid environment and utilizes Agentic Capabilities and your existing security tools to rapidly mitigate and remediate the 10% most likely to be exploited. ​​Who’s behind us: Zafran is backed by Menlo Ventures, Sequoia Capital, Cyberstarts, and a deep belief that cybersecurity should move as fast as attackers do. We’re one of the fastest-growing companies in the industry, scaling to meet demand from the world’s most advanced, security-obsessed organizations. We’re serious about our mission- so expect work that matters, teammates who challenge and inspire you, and plenty of fun along the way! What you will do: • Design, implement, and manage scalable, highly available databases for both transactional, real-time use as well as backend analytics and big data. • Build, design, and maintain data processing pipeline infrastructure used by our various applications • Develop and maintain CI/CD processes for automating seamless changes to data infrastructure, including data migrations and database upgrades • Work closely with developers to make sure our data infrastructure meets existing and new feature requirements • Work alongside our Security Team to ensure our infrastructure meets rigorous security standards • Ensure the data infrastructure processes adhere to best practices in security and compliance. Requirements: • 5+ years of experience as a Data Infrastructure Engineer or in a related role (Such as DevOps with expertise in data). • Proficiency with various database systems, including: Postgres, Elasticsearch, ClickHouse, and Neo4j. • Proficiency working with Kafka. • Hands-on experience with managing databases in a self-hosted environment, preferably on Kubernetes. • Proven experience with cloud environments such as AWS, Google Cloud, or Azure. • Knowledge of containerization and orchestration tools like Docker and Kubernetes. • Experience with Infrastructure as Code tools like Terraform and CloudFormation. • Hands-on experience with monitoring and logging tools such as Prometheus, Grafana, ELK Stack, and Datadog. • Excellent problem-solving and troubleshooting skills. • Experience with disaster recovery planning and high-availability architectures • Professional certifications showing deep knowledge in Elasticsearch, ClickHouse, and relational databases are a plus.

JLL empowers you to shape a brighter way. Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. Senior Data Engineer – Job Description Role summary: As a Senior Data Engineer, you will lead the development of scalable data pipelines and integration of diverse data sources into the JLL Asset Beacon platform. Your work will focus on consolidating financial, operational, and leasing data into a unified platform that delivers accurate insights for commercial real estate asset management. Collaborating closely with internal developers and stakeholders, you will gather requirements, solve integration challenges, and ensure seamless data flows to support informed decision-making. In addition to technical responsibilities, you will mentor junior engineers, promote best practices in data engineering and maintain high-quality standards through code reviews. Leveraging tools like Spark, Airflow, Kubernetes, and Azure, you will enhance the platform's performance, reliability, and scalability. Your expertise in data ecosystems will play a critical role in driving innovation and enabling advanced data-driven solutions for the evolving needs of the real estate industry. Company bio: JLL Asset Beacon is transforming commercial real estate asset management through data integration and innovation. Our SaaS platform consolidates and reconciles data across financial, operational, and leasing functions, creating a single source of truth. By providing real-time, end-to-end visibility into asset, fund, and portfolio performance, we empower real estate professionals to make faster, more informed decisions. With robust data visualization and reporting capabilities, our platform simplifies complex data ecosystems, enabling seamless collaboration and unlocking opportunities for value creation. Responsibilities: • Data pipeline development• Design and implement scalable, efficient, and robust data pipelines • Data platform management• Support and main the data platform to ensure reliability, security, and scalability. • Collaborate with internal developers and stakeholders• Work closely with internal developers and stakeholders to gather requirements, deliver insights, and align project goals. • Mentorship and leadership• Mentor junior engineers, fostering their growth through knowledge sharing and guidance • Conduct code reviews to maintain quality and consistency Our Technologies: • Data Processing: Spark • Workflow Orchestration: Airflow • Containerization: Kubernetes • Cloud: Azure • Data APIs and Semantic Layer: CubeJS The Candidate: • Educational Background: A STEM degree, preferably in Computer Science or Computing. • Professional Experience: At least 5 years of experience in data engineering, data warehousing, or a related field. • Technical Proficiency:• Strong Python and PySpark experience • SQL skills are essential • Experience with data orchestration platforms or tools such as Airflow, ADF, or SSIS • Hands on experience using AI powered development tools (e.g. AI coding assistants, code generation or review copilots) to build, test, and ship production code, and comfort applying AI/ML techniques within data pipelines and platform features • Data Expertise:• Solid understanding of data modeling principles and data warehousing concepts. • Domain Knowledge: Financial or real estate experience is advantageous but not required. Location: On-site –TEL AVIV, ISR If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements. We’re interested in getting to know you and what you bring to the table! At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you're pursuing. JLL Privacy Notice Jones Lang LaSalle (JLL), together with its subsidiaries and affiliates, is a leading global provider of real estate and investment management services. We take our responsibility to protect the personal information provided to us seriously. Generally the personal information we collect from you are for the purposes of processing in connection with JLL’s recruitment process. We endeavour to keep your personal information secure with appropriate level of security and keep for as long as we need it for legitimate business or legal reasons. We will then delete it safely and securely. For more information about how JLL processes your personal data, please view our Candidate Privacy Statement. For additional details please see our career site pages for each country. Jones Lang LaSalle (“JLL”) is an Equal Opportunity Employer and is committed to working with and providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process – including the online application and/or overall selection process – you may email us at [email protected]. This email is only to request an accommodation. Please direct any other general recruiting inquiries to our Contact Us page > I want to work for JLL.

Location Our Data Engineer will be an integral part of our Attribute Engineering teams. This role is based fully on-site from the Attribute Tel Aviv office, located in Carlibach St 29 Tel Aviv, center. Who We Are DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well-architected and scalable state - from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide. About Attribute™ Meet Attribute™: AI tokenomics without tags, SDKs, or code changes. A lightweight eBPF sensor installs in 15 minutes and starts producing per-customer, per-feature, and per-agent cost the same day. No instrumentation. No tagging policy. No engineering lift. The problem it solves is real and measurable. In DoiT's survey of 500 finance and technology leaders, only 15% said they could calculate AI ROI without significant bottlenecks — and 79% experienced AI-related cost overruns in the past 12 months. AI spend is tripling. Visibility isn't keeping up. Attribute™ fixes that, at the kernel level, with nothing to instrument. The Opportunity Attribute technology and services are heavily based on eBPF technology on the Attribute sensor side and extensive ETL and data processing on the Backend (based on GCP). In order to meet the increasing challenges of the growing business, we are seeking an experienced engineer who will be working closely with our product on one hand and with the Frontend team on the other, and will be responsible for development of new capabilities as well as operational aspects of services you develop. Responsibilities • Implementing features. From proposal through spec and implementation to maintenance. You're expected to propose things you think can be an excellent addition to the products. • Reviewing code. We believe in code reviews, and you'll soon start reviewing pull requests as well. • Providing feedback. The team plans and discusses upcoming work. We provide feedback to each other, trying to find challenges and unknowns as early as possible. • Working AI-augmented. Use AI daily across your workflow, exploring unfamiliar codebases, prototyping, debugging, writing tests, and documentation. • Collaborating cross-functionally. Work with product, support, and other engineers to understand where the gaps hurt, and bring your own ideas to the table rather than waiting for a spec. Qualifications • 3+ years of hands-on experience in data engineering, backend engineering, or a similar field • 2+ years of practical experience with Python, Spark, and PySpark • Experience building ETL processes and working with big data technologies such as Pub/Sub • Familiarity with Terraform and CI/CD methodologies • Experience building or operating data pipelines in a SaaS production cloud environment • Versatile developer who learns new tools and technologies quickly, uses AI effectively, and applies sound engineering judgment • Strong collaborator and independent contributor with excellent communication skills • Growth minded, pragmatic problem solver who simplifies complexity and iterates quickly from MVPs using data and feedback • Excellent communication skills in English, both written and verbal • Self-organized, goal-oriented and self-motivated; confident, thorough and tenacious Bonus Points • BA/BS degree or equivalent practical experience • Experience with C++ or C# • Familiarity with Google Cloud Platform Are you a Do’er? Be your truest self. Work on your terms. Make a difference. We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally. What does being a Do’er mean? We’re all about being entrepreneurial, pursuing knowledge and having fun! Click here to learn more about our core values. Sounds too good to be true? Check out our Glassdoor Page. We thought so too, but we’re here and happy we hit that ‘apply’ button. • Unlimited Vacation • Flexible Working Options • Health Insurance • Parental Leave • Employee Stock Option Plan • Home Office Allowance • Professional Development Stipend • Peer Recognition Program Many Do’ers, One Team DoiT unites as Many Do’ers, One Team, where diversity is more than a goal—it's our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success. #LI-Remote

Description About DoorLoop DoorLoop is property management software built for speed and the smart choice for people who take growth seriously. With offices in Miami, New York City, and Tel Aviv, we’re a global company helping property owners and managers move faster, scale smarter, and get real support, real fast. We’re proudly People First. That’s why we’re a Certified Great Place to Work, recognized by Forbes as one of America’s Best Startup Employers in both 2024 and 2025, and earn top ratings on Glassdoor. We’re growing quickly and looking for a Motion Designer to help elevate our brand in the market. Mission DoorLoop is looking for a Senior Data Engineer to co-own our data platform as its second engineer. Part of the foundation is in place: a layered dbt project on Snowflake, a semantic layer that defines our core business metrics once, and an AI data assistant that answers business questions from it. Much of it is still being designed, and you will help build it. You will take design decisions with our founding data engineer, own whole business domains end to end, and raise the bar on modeling, reliability, and review as more of the company depends on the platform. You will also build data products with AI: our AI data assistant, internal dashboards, and the data apps that come next, built AI-first and tested so their answers can be trusted. You will work directly with Finance, GTM, and CX leaders on the metrics that run the business, and with analysts who ship their own dbt changes, helping them do it well. This role is for an experienced engineer who wants to shape the foundation while it is still being built: open-minded, willing to push back on the right questions, and ready to make design decisions and own them. Responsibilities • Own core business domains end to end (GTM, CX, and Finance), from the stakeholder conversation to the model, the metric, and the dashboard or AI answer built on it • Help build the foundation: take design decisions with the founding engineer on modeling patterns, incremental strategy, ingestion design, semantic-layer structure, and warehouse cost • Lead metric-definition work with business leaders: settle contested definitions, document them, and get sign-off • Own platform reliability: testing strategy, monitoring and alerting, the incident process, and root-cause fixes • Design and build ingestion for new sources, choosing the right pattern (Fivetran, Airbyte, or custom n8n → S3 → Snowpipe pipelines) • Reconcile financial data (Stripe payments and payouts) to its source of truth, and own the checks that keep revenue numbers right • Extend the semantic layer: definitions, descriptions, and verified queries, so every tool gives the same answer • Build data products with AI: the AI data assistant that answers business questions, internal dashboards, and new data apps. Design them AI-first, and build the evaluation sets that prove their answers are correct • Set the standard for code review, and mentor the analysts who ship their own dbt changes • Use AI tools every day, and build the shared skills and workflows the data team relies on Requirements • 5+ years in data engineering or analytics engineering, including at least 2 years owning a production data warehouse or dbt project • Expert SQL and deep experience with a cloud data warehouse (Snowflake preferred), including performance and cost • You have designed and led a production dbt project: structure, conventions, testing, and CI • You have built and run ingestion pipelines, with managed tools and with your own code • You have owned data reliability: monitoring, alerting, incident response, and the fixes that follow • You have led metric-definition work with senior non-technical stakeholders, such as Finance • Strong Python for pipelines, automation, and tooling • You review other people’s code and mentor less experienced colleagues • You use AI tools (Claude, Cursor, or similar) every day, with specific workflows you can describe and defend • You want to build data products with AI, not only use AI to write code • Open-minded: you ask before you assume, and you change your mind when the evidence says so • You push back on the right questions, and you make design decisions and own them • Clear, direct communicator, in writing and in conversation Bonus Points • Built or owned a semantic layer (Snowflake Semantic Views, dbt Semantic Layer, Cube, LookML, or similar) • Payments or financial data: Stripe, revenue recognition, reconciliation, month-end close • Shipped a data product with AI: an AI assistant, text-to-SQL, an LLM feature on data, or evaluation sets for AI answers • Orchestration and automation tools (n8n, Airflow, Dagster, or similar) and AWS Benefits What We Offer • Hybrid work model (4 days in office, 1 WFH day) • Unlimited paid time off • Cibus meal card • Private health insurance subsidy • Pension, Keren Hishtalmut & Dmei Havraa • Real impact at scale — product used daily by thousands of paying customers • Certified Great Place to Work · Forbes America's Best Startup Employers 2024 & 2025 Equal Opportunity Employer & AI DoorLoop is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, marital status, pregnancy, genetic information, citizenship status, or any other characteristic protected by applicable law. We use AI-assisted tools, including assessment tools that evaluate cognitive ability and personality fit, to help evaluate applications. Human oversight is maintained and no hiring decision is made solely by an automated system. For positions that can be performed in New York City, candidates may request an alternative selection process. Applicants with disabilities may also request reasonable accommodation at any stage of the hiring process. For any of these requests please contact [email protected]. For more information about the tools we use, bias audit results, and how we collect and protect applicant personal data, please review our Applicant Privacy Notice.

Simply's mission is to make the pursuit of creative hobbies simple and fun for all. Our top-grossing apps Simply Piano, Simply Guitar, Simply Sing, and Simply Draw put us on the fast track to building the world's largest consumer subscription service for creative learning. With over a million monthly downloads and hundreds of thousands of daily learners, we're building the AI creative companion for every home. As a Data Engineer You Will: • Design, develop, scale, and optimize the data pipelines, ETLs and platform components (inc infra for analytics AI) • Implement and support robust, scalable data solutions from different sources to enhance business analysis capabilities. This includes implementing technologies from scratch and continuously exploring best practices and tools. • Help to improve our analytics work. By getting better data, making it more trustworthy and accessible and help nurturing top-grade data culture in the company. • Collaborate with various stakeholders across the company to translate requirements into high-quality data pipelines and models Who are you? • Have 5+ years of experience as a BI Developer or Data Engineer including ETL design, implementation, optimization and maintenance. • Experienced in writing complex SQL queries, Data Modeling, and Python. • Proven record of merging multiple data sources to develop a single source of truth for reporting needs • Experience working with CI/CD systems • Excellent communication skills and comfortable working in a multi-disciplinary team • Experience preparing data for business intelligence tools such as Tableau • Bachelor’s or Master’s Degree in Computer Science, Information Systems or a related field • An independent, self-learner who is passionate about data • Understands business processes and can translate business needs into software solutions • Knowledge of metadata management, data lineage, and principles of data governance • A team player with amazing verbal and written communication skills Bonus Points if you have: • Understanding of current marketing analytics topics including attribution and experience with advertising campaign management/buying platforms. • DBT experience. • Advanced Snowflake skills.

Description Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles. Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year. Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally. Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics. About the Role  We're growing our Data team and looking for a Data Engineer who lives and breathes data. At Appdome, data moves fast and at scale: trillions of live threat events feeding real-time detection, risk scoring, and response. This role is about designing data so it stays fast under heavy volume: the right architecture, models, and storage choices to keep latency low and throughput high. You'll own data systems end to end, from ingest to serving, and have real influence over how we build. What You’ll Do  • Design, build, and maintain scalable, low-latency data pipelines, ETL/ELT, and infrastructure. • Architect data models, schemas, and storage layouts optimized for fast retrieval at large volume. • Build real-time / event-driven ingestion and processing for high-throughput threat and risk data. • Optimize SQL and NoSQL databases - query tuning, indexing, partitioning, cost/performance tradeoffs. • Work with big-data volumes across pipelines, data lakes, and warehouse / lakehouse layers, using Spark for large-scale processing and enrichment. • Own data observability - pipeline health, freshness, and quality. • Collaborate with software engineers, security experts, and data scientists to integrate data into Appdome's products. Requirements • B.Sc. in Computer Science, Data Engineering, or a related field. • 3+ years hands-on with large-scale data infrastructure. • Strong Python (including Pandas). • Deep SQL and NoSQL knowledge, with real performance-optimization experience. • Proven experience with real-time/low-latency systems, and the data architecture and modeling instincts that make them fast. • Comfort with big-data volumes, pipelines, and data lakes. • Experience with streaming / event-driven design (e.g., Kafka). • Spark / PySpark for large-scale processing, or a strong adjacent big-data background. • Strong problem-solving skills and a proactive, independent mindset. Advantages  • Experience with ClickHouse. • Data warehousing and lakehouse / open table formats (e.g., Apache Iceberg). • Vector databases and embeddings - storing and serving them to support AI and data-science features (e.g., pgvector). • Strong system design and data architecture sense - knows how to build the data model the right way. • Experience using AI-assisted development tools (e.g., Cursor, Claude Code) in day-to-day work. • Experience with security, fraud, or other high-volume telemetry data. • Comfort with containerized infrastructure (Docker, basic Kubernetes) and light DevOps (CI/CD, Git). • ElasticSearch, Redis, DynamoDB, or Metabase. Who you are  • Independent and self-driven - comfortable owning systems end to end. • Growth-oriented - eager to develop and take on more over time. • Collaborative - communicates well across engineering, security, and data science. • Adaptable - thrives in a fast-paced environment with a can-do attitude.

Description Join Noma Security and help shape the future of AI security in a fast-paced, high-impact environment. Noma is building the first comprehensive AI Security and Governance Platform, helping organizations adopt AI and LLMs with confidence and control. As AI becomes core to modern enterprises, we provide seamless visibility and protection across the entire AI lifecycle. Our team works at the intersection of AI, security, and cloud, shaping a new category in a rapidly evolving market. At Noma, you’ll have the opportunity to make a meaningful impact while working with exceptional people on technology that truly matters. As Noma's Founding Data Engineer, you will be the first dedicated Data Engineer at the company, with the opportunity to build our data engineering function from the ground up. You'll own the architecture and foundations of Noma's internal data platform - the central warehouse, pipelines, and models every team relies on to understand how our product is performing, and provide tools for day to day operations. You'll define how data from across the company is collected, modeled, transformed, and governed, and establish the standards and best practices that will scale with Noma as we grow. Working closely with Engineering, Data Science, Delivery, Product, and Analytics teams, you'll turn data into reliable, structured assets that power product and business decisions. 🛠️ What You’ll Do • Build Noma’s Data Engineering Function: Be the first dedicated Data Engineer at Noma and will be required to be opinionated about the warehouse architecture, tooling, standards, and best practices for how we work with data across the company. • Develop Data Pipelines: Design, build, and maintain data pipelines that transform company data into structured, queryable assets • Enable Data-Driven Decisions: Partner with Engineering, Data Science, Product and Analytics teams to deliver trusted data foundations and actionable insights. • Create BI Dashboards: Build and maintain dashboards and reporting layers that support product, and operational decision-making • Ensure Data Quality and Accessibility: Implement best practices for data modeling, governance, monitoring, and reliability ✅ What You Bring • 5+ years of experience in Data Engineering roles • Proven experience designing and building data infrastructure from the ground up, with the ability to independently drive architecture and technology decisions. • 3+ years of experience working with modern data warehouse technologies such as Snowflake or Databricks • Strong SQL skills and experience working with large-scale datasets • Experience creating data models and BI dashboards serving business, product, or analytics teams • Strong understanding of data architecture, performance, and scalability considerations • Ability to work cross-functionally with technical and non-technical stakeholders • Strong ownership mindset and a pragmatic approach to problem solving 🎯 Nice to Haves • Experience working closely with Data Science teams • Background in cybersecurity, infrastructure, deep tech, or AdTech environments • Experience as an early or first Data Engineer at a startup, or building a data platform from an early stage. • Familiarity with modern data orchestration and transformation tools 💙 Perks & Benefits • Located in a prime area with easy access to public transportation, near the Hashalom train station and the Light Rail. • A bright, spacious, dog-friendly office that makes you smile the moment you walk in 🐶 • Team bonding activities, company retreats, happy hours, and holiday gifts • Pilates classes at the office 💪🧘‍♀️ • Never hungry: Monthly lunch budget, dinners provided, and a fully stocked kitchen 🍽️ • A warm, supportive, and people-first company culture • Pension & Keren Hishtalmut (advanced training fund) • A global work environment with teams across Tel Aviv, the US, the Netherlands, and London • Options that let you share in Noma’s growth and success

Transmit Security gives businesses the modern tools they need to build secure, trusted and end-to-end digital identity journeys to innovate and grow. CX-focused, cybersecurity conscious leaders rely on Transmit Security’s xCIAM platform to provide their customers with smooth experiences protected from fraud across all channels and devices. Transmit Security serves many of the world’s largest banks, insurers, retailers, and other leading brands, collectively responsible for more than $1.3 trillion in annual commerce. About the Role: Transmit Security is a top player in its game and is heading for an exciting year. In this role, you will take an integral part in the creation of a new and innovative SaaS product. If you are a talented software engineer that likes to solve complex problems and looking for your next challenge we want you to join our team! What you’ll do: As the Team Leader at Transmit Security, you will lead a talented team to design, implement, and deploy scalable data solutions for an innovative SaaS product. You’ll be responsible for shaping the direction of a system that impacts millions of users worldwide. If you're an experienced manager with a strong technical background, ready to lead in a dynamic, fast-paced production environment, we want you on our team! Responsibilities: • Collaborate effectively with cross-functional teams (Data Science, Security, etc.) to ensure alignment on business needs and security standards. • Manage the technical backlog, make critical decisions on product milestones, and ensure timely, secure, high-quality delivery and deployment. • Foster the growth of team members through mentorship, personal development opportunities, and the promotion of continuous improvement. • Identify, manage, and mitigate security risks across the data platform, maintaining high standards in all initiatives. • Define and implement methodologies to guarantee the performance, scalability, and quality of our data infrastructure. What We're Looking For: • 5+ years of software engineering experience, with at least 2 years in a leadership capacity. • Extensive knowledge of distributed computing platforms (Flink, Spark, Beam) and cloud platforms (AWS, GCP, Azure). • Proficiency in programming languages such as Python, Java, Scala, or Go. • Demonstrated strong leadership abilities, including guiding teams, managing backlogs, and fostering effective cross-functional collaboration. • Proven experience in managing security risks and ensuring high-quality, scalable, and compliant systems. • Ability to maintain focus on key priorities and excel in a fast-paced, dynamic production environment. Advantages: • Bachelor’s degree in Computer Science or a related field. • Experience with microservice architecture, data management tools (Kafka, Airflow, MLOps), and CI/CD processes. • Familiarity with databases (Postgres SQL, Redis) and Kubernetes. • Background in identity management, risk assessment, or fraud detection. #LI-AM1 #LI-Hybrid

Description At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we have pioneered the world's first fully automated suite of vehicle inspection systems. At the heart of this innovation lies our advanced AI-driven technology, representing the pinnacle of machine learning, GenAI, and computer vision within the automotive sector. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone. We are looking for an experienced Senior Data Engineer to join our Data team. In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data/analytics platforms. A day in the life and how you’ll make an impact: • Design and develop high-performance Data Pipelines and ETL processes to support diverse business needs. • Work closely with Product, Business Intelligence, Sales and other teams to integrate data solutions, ensuring seamless alignment and collaboration across functions. • Continuously improve our data analytics platforms, optimizing system performance and ensuring robust, reliable data infrastructure. • Oversee the entire data lifecycle, from infrastructure setup and data acquisition to detailed analysis and automated reporting, driving business growth through data-driven insights. • Implement robust data quality checks, monitoring mechanisms, and data governance policies to maintain data integrity and security, troubleshooting and resolving any data-related issues efficiently. Requirements • 5+ years of hands-on experience in Data Engineering, preferably in a fast-paced startup or technology environment. • Strong experience designing, building, and maintaining scalable data pipelines and ETL/ELT workflows. • Experience with modern data engineering technologies such as Airflow, Spark, Flink, Kafka, Pub/Sub, and dbt – an advantage. • Experience with cloud data warehouses such as BigQuery or Snowflake, and strong hands-on experience with public cloud environments, preferably GCP or AWS. • Hands-on experience with GenAI technologies and AI-assisted engineering tools, applying them to development, data workflows, automation, debugging, and engineering productivity. • Strong SQL expertise and hands-on experience with relational and NoSQL databases such as MySQL, Firestore, Redis, and Elasticsearch. • Strong Python development skills. • Experience with BI and visualization platforms such as Looker or Tableau. • Strong analytical and problem-solving skills, with the ability to communicate technical ideas and insights clearly to both technical and business stakeholders. • B.Sc. in Computer Science, Information Systems Engineering, Software Engineering, or a related technical field – an advantage. • Ability to work from our Tel Aviv office 4 days a week. Why UVeye: • Pioneer Advanced Solutions: Harness cutting-edge technologies in AI, machine learning, and computer vision to revolutionize vehicle inspections. • Drive Global Impact: Your innovations will play a crucial role in enhancing automotive safety and reliability, impacting lives and businesses on an international scale. • Career Growth Opportunities: Participate in a journey of rapid development, surrounded by groundbreaking advancements and strategic industry partnerships.