Data Engineer jobs in Israel

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.

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.

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

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.

Description Trigo is Crafting Tomorrow's Retail Experience We started with a simple question: How can vision AI make shopping better for everyone? Today, our team of passionate experts is transforming how people shop and how retailers operate, one store at a time. Our goal is to solve real retail challenges by developing advanced computer vision AI that prevents loss, enables grab-and-go shopping, and turns store data into valuable insights, all while delivering clear value from day one. With multiple global deployments, deep partnerships with leading retailers, and a product suite that’s proving its impact every day, Trigo isn’t just imagining the future of retail, we’re building it. We are looking for an experienced Data Engineer to own and evolve Trigo’s data infrastructure and analytics platform. You will design and build scalable data pipelines and data models, working with large and diverse datasets generated across Trigo’s global retail deployments. You will own the data lifecycle from ingestion and transformation through orchestration, reliability, and analytics-ready datasets. As a key technical owner of Trigo’s data platform, you will also work closely with analysts, Product, R&D, Operations, and Customer Success to build trusted data products, evolve our BI semantic layer, and turn complex data into scalable solutions A day in the life • Design, build, and maintain scalable ETL/ELT pipelines processing large and diverse datasets. • Own and evolve Trigo’s data architecture, from ingestion and transformation through analytics-ready data models and consumption. • Build reliable data workflows and orchestration processes, with a focus on scalability, performance, and maintainability. • Design and maintain curated data models that power analytics, operational workflows, and data-driven products. • Ensure data quality, observability, monitoring, and reliability across datasets and pipelines. • Develop and evolve the BI semantic layer, enabling trusted metrics and self-service analytics across the company. • Work closely with R&D and Product on data integrations and new data-driven capabilities, including use cases that bridge offline analytics and production systems. • Partner with analysts and business stakeholders to translate complex requirements into scalable data solutions. You bring to the table • 4+ years of experience as a Data Engineer, Analytics Engineer, or similar hands-on data role • Strong command of SQL and proficiency in Python for data modeling and transformation • Experience with modern data tools such as dbt, BigQuery, and Airflow (or similar) • Proven ability to design clean, scalable, and analytics-ready data models • Familiarity with BI modeling and metric standardization concepts • Experience partnering with analysts and stakeholders to deliver practical data solutions • A pragmatic, problem-solving mindset and ownership of data quality and reliability • Excellent communication skills and ability to connect technical work with business impact Nice to have • Experience implementing or managing BI tools such as Tableau, Looker, or Omni. • Understanding of retail, computer vision, or hardware data environments. • Exposure to time-series data, anomaly detection, or performance monitoring. • Interest in shaping a growing data organization and influencing its direction.

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 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

About Tavily - Search API Tavily is a cutting-edge company focused on providing a powerful and efficient Search API. We empower developers and businesses by delivering high-quality, relevant search results quickly and reliably. Join our team to help build the data infrastructure that supports and scales our core product. The Role We are seeking a highly motivated and experienced Data Engineer to join our growing team. You will be responsible for designing, constructing, installing, testing, and maintaining highly scalable data management systems. You will work closely with our engineering and devops teams to build and optimize the data pipelines that are crucial to the performance and accuracy of our Search API. Responsibilities • Design, build, and maintain efficient and reliable ETL/ELT pipelines for data warehousing. • Develop and improve our data infrastructure. • Ensure data quality, integrity, and security across all data platforms. • Optimize data systems for performance and scalability. • Troubleshoot and resolve issues related to data pipelines and data infrastructure. • Collaborate with cross-functional teams to understand data needs and deliver solutions. Minimum Qualifications • A degree in Computer Science, Statistics, Engineering, or a related quantitative field. • 3+ years of professional experience as a Data Engineer or in a similar role focused on data infrastructure. • Proficiency in Python. • Solid experience with relational databases (SQL) and NoSQL databases. • Experience with AWS and their data services. • Proven experience in building and optimizing data pipelines and data architectures. Preferred Qualifications • Experience with Mongo/Snowflake/Redis/S3 General/S3 Express. • Experience with big data technologies • Experience with Airflow. • Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes). • Experience working in a company focused on API services or search technology. • Knowledge of data governance and data security best practices.

Description Vega is one of the fastest-growing startups in cybersecurity, redefining security analytics and operations with an AI-native platform for the SOC. We are building the next-generation operating system for security teams. Vega is already delivering real impact at some of the world’s largest organizations - improving detection, unlocking the value of their security data, and reducing cost and complexity. With HQs in New York and TLV, we're looking for people who want to be a part of the next rocket-ship in cyber. We’re looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, you’ll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data. We’re seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems. WHAT YOU WILL DO • Design, build, and operate scalable data pipelines (batch + streaming) that power Vega’s AI systems in production. • Own the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement. • Build and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale. • Ensure high data quality and high-standard operation through monitoring, alerting, and validation checks. • Work with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads. • Partner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure. • Define and evolve the data strategy to for AI applications. Requirements WHAT YOU WILL BRING • 4+ years of professional experience in data engineering or ML infrastructure roles. • Strong experience designing and building data pipelines (batch and streaming) for large-scale production systems. • Hands-on experience with data storage systems such as data lakes, data warehouses, relational databases and graph databases. • Proven ability to build reliable, observable, and scalable data infrastructure, including monitoring, alerting, and data quality checks. • Demonstrated ownership across the full data lifecycle from ingestion and modeling, to serving, monitoring, and continuous improvement. • Ability to work independently, managing priorities effectively in a fast-paced, product-driven environment, according to a dynamic data strategy. • Experience with modern data and infrastructure technologies such as DuckDB, dbt, Temporal, Trino, Spark, PostgreSQL, PGVector Neo4j, Datadog, Python, Go, Docker, and Kubernetes. • Experience working with ML models and framework as part of data pipelines (e.g. text embedding models, vector databases, semantic search algorithms) NICE TO HAVE • Experience building data infrastructure to support ML/AI systems, including feature extraction pipelines for downstream ML models and inference-time data access. • Background in working with high-volume or complex data sources such as logs, events, telemetry, or security data. • Familiarity with modern cloud platforms, preferably AWS, and cloud-native data tools. • Experience collaborating closely with ML/AI engineers to translate model and research requirements into scalable data solutions.

Description Deloitte’s the leading and largest consulting firm in the world, is looking for a Data Engineer to join our Data and AI Practice. We are building cutting-edge data solutions that introduce next level automations and aim for low-code/no-code data platforms that allows our customers to deliver fast while hiding all the engineering complexities behind. If you are not afraid of any engineering task, likes to build cool stuff and enjoy when customers use your craft – you belong with us. Requirements • Proven experience with Big Data projects and in depth knowledge with GCP BigQuery. Certifications are desired • Experience building cloud-scalable, real-time and high-performance Data Solutions of 3+ years. • Programming Skills with Spark, Python. • Solid engineering foundations (good coding practices, good architectural design skills). • Proven experience with building data pipelines, preferably with Airflow/Composer, Dataform/DBT, Spark, Dataplex • Solid cloud development experience in GCP (and/or others). Certifications are desired • Advanced SQL knowledge and capabilities • Big Advantage – Experience with Databricks • Experience or knowledge in AI/GenAI/ML/MLOps/LLM - advantage • Team player, positive, likes to dream big and fulfill dreams all at the same time • Experience and/or ability to work with AI tools – mandatory Location: Tel Aviv, Hybrid. We at Deloitte believe that diversity and inclusion among our people is a critical component of our success, which is why we cultivate an organizational culture that embraces diversity in all its forms.

Description DRS RADA is a leading defense high-tech company specializing in radar systems development. We are seeking an experienced Data Engineer to join our data engineering team. In this role, you will play a crucial part in designing, developing, and maintaining scalable data pipelines and infrastructure to support our AI department. This is an opportunity to work with cutting-edge technologies in a fast-paced production environment, driving impactful, data-driven solutions for the business. Key Responsibilities: • Design, develop, and optimize ETL/ELT pipelines for large-scale data processing. • Work with a modern data stack, including Databricks (Spark, SQL), Apache Airflow, and Azure services. • Troubleshoot and optimize queries and jobs for performance improvements. • Implement best practices for data governance, security, and monitoring. • Stay updated with industry trends and emerging technologies in data engineering. If you're passionate about building scalable data solutions and thrive in a fast-paced environment, we’d love to hear from you! Requirements • 4 years of experience in data engineering or related fields. • Proficiency in Python for data processing and automation (mandatory). • Deep understanding of Apache Spark and Databricks for big data processing (mandatory). • Experience with Git (mandatory). • Expertise in Apache Airflow for workflow orchestration. • Familiarity with cloud-based environments, particularly Azure. • Advanced proficiency in SQL and query optimization. • Familiarity with data modeling, ETL/ELT principles, and performance tuning. • Knowledge of CI/CD and containerization (Docker). • An enthusiastic, fast-learning, team-oriented, and motivated individual who loves working with data.

Description We Are KPMG Israel. KPMG Israel is more than a professional services firm, we are a community of people driven by impact. As part of a leading global network, we partner with the most influential organizations to drive large-scale business and technology transformation, combining strategic thinking, innovation, and advanced technologies. Guided by integrity, excellence, and forward thinking, we help organizations navigate complexity and create real change. Our people are at the center, with space to lead, experiment, and grow in a dynamic and collaborative environment that connects personal development with meaningful impact on the future of business and society. We are seeking a skilled and motivated Data Engineer with expertise in Elasticsearch, cloud technologies, and Kafka. As a data engineer, you will be responsible for designing, building and maintaining scalable and efficient data pipelines that will support our organization's data processing needs. The role will entail: • Design and develop data platforms based on Elasticsearch, Databricks, and Kafka • Build and maintain data pipelines that are efficient, reliable and scalable • Collaborate with cross-functional teams to identify data requirements and design solutions that meet those requirements • Write efficient and optimized code that can handle large volumes of data • Implement data quality checks to ensure accuracy and completeness of the data • Troubleshoot and resolve data pipeline issues in a timely manner Requirements • Bachelor's or Master's degree in Computer Science, Engineering, or a related field • 3+ years of experience in data engineering • Expertise in Elasticsearch, cloud technologies (such as AWS, Azure, or GCP), Kafka and Databricks • Proficiency in programming languages such as Python, Java, or Scala • Experience with distributed systems, data warehousing and ETL processes • Experience with Container environment such AKS\EKS\OpenShift is a plus • high security clearance is a plus The position is open for all genders as well as people with disabilities.

Description CodeValue is looking for an experienced, hands-on Senior Backend Engineer with strong expertise in Python and data-intensive systems to join our development team. In this role, you will design, develop, and maintain backend services and reliable data pipelines, build integrations with external systems, and work with modern cloud technologies and AI-powered development tools. You will collaborate closely with engineering, data, and implementation teams to deliver scalable, high-quality software solutions. Key Responsibilities • Design, develop, and maintain backend services and production-grade data pipelines. • Build and operate batch and event-driven ETL processes, including data transformation, validation, and schema management. • Design and maintain data models and schemas that serve as a reliable source of truth across systems. • Develop integrations with external systems and data sources, including API-based integrations and structured data files. • Build reusable integration frameworks and tools to streamline data onboarding, validation, and troubleshooting. • Work with relational and document databases, ensuring data consistency, reliability, and performance. • Implement monitoring, logging, alerting, and observability for production systems. • Ensure data security, auditability, and appropriate access controls in compliance-sensitive environments. • Collaborate with backend, DevOps, data, and other cross-functional teams throughout the software development lifecycle. • Leverage AI-assisted development tools to improve engineering productivity and code quality. Requirements • 5+ years of hands-on experience in backend software development, with significant experience building data pipelines or data-intensive systems. • Strong proficiency in Python, including asynchronous programming and modern web frameworks such as FastAPI. • Proven experience designing, developing, and maintaining production data pipelines, including batch and event-driven ETL. • Hands-on experience with AWS services such as S3, SQS, Lambda, RDS/PostgreSQL, and DynamoDB. • Experience deploying and maintaining containerized services in Kubernetes environments. • Strong SQL skills and practical experience with relational and document databases. • Experience with Python data-processing tools such as pandas and Parquet, as well as schema validation libraries such as Pydantic. • Experience owning production systems end to end, including monitoring, alerting, troubleshooting, and incident handling. • Experience using AI-powered coding tools such as Claude Code or Cursor as part of the daily development workflow. • Experience working in Agile development environments. • Bachelor's or Master's degree in Computer Science or a related field. • Strong analytical and problem-solving skills, a data-oriented mindset, and a proactive approach to delivery. • Excellent written and verbal English communication skills. Advantages • Experience working with healthcare data standards and integrations, such as HL7v2, FHIR, Epic, or Oracle Health. • Experience integrating workforce management or scheduling platforms, such as UKG or QGenda. • Familiarity with handling sensitive data and security frameworks such as HIPAA or SOC 2. • Experience building multi-tenant SaaS platforms. • Experience with data governance, audit logging, and tenant isolation.