Data Engineer jobs in Israel

Join a leading financial organization as a Data Developer in the Banking Applications Division. In this role, you will take part in developing and maintaining data staging processes within a Oracle-based environment, supporting critical financial systems and business intelligence solutions. You will work closely with technological teams and business stakeholders, contributing to the delivery of high-quality data solutions in a dynamic and data-driven environment. This position requires strong analytical capabilities, hands-on development experience, and the ability to work both independently and as part of a team. What You'll Do: Develop and maintain Stage Data processes in an Oracle environment. Write and optimize complex SQL and PL/SQL queries and functions. Support data flows and integrations for financial systems. Collaborate with BI teams and business units to ensure data accuracy and availability. Troubleshoot and resolve data-related issues while meeting deadlines. Work in a multi-system environment with ongoing learning and development. Qualifications: 2+ years of experience working with Stage Data in an Oracle environment - Must Strong experience in writing complex queries and functions using PL/SQL - Must Experience working with BI systems in the financial/banking domain - Significant advantage Experience with Angular - Advantage Bachelor's degree in a relevant field - Advantage Key Skills Required for Success in the Role: Ability to work both independently and as part of a team Strong analytical thinking and problem-solving skills High service orientation and interpersonal communication skills Ability to work in a dynamic, multi-interface environment Fast learning abilities and adaptability to new systems and technologies

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Description We are looking for a talented and experienced Mid-Level Data Engineer with a passion for data and cloud technologies to join our team. In this role, you will be a key player in designing, developing, and maintaining our AWS and Databricks-based data platform. You will bridge the gap between robust Data Engineering, Database Administration (DBA), and high-performance Analytics Engineering. The ideal candidate understands the Lakehouse architecture, has experience managing modern cloud data warehouses like Snowflake, and possesses the ability to build scalable ETL pipelines that drive data-driven business decisions. Key Responsibilities • End-to-End Pipelines: Develop and maintain complex ETL/ELT pipelines using Python/PySpark on the Databricks platform, as well as loading and transforming data within Snowflake. • Data Architecture: Implement and manage data layers following the Medallion architecture (Bronze, Silver, Gold) using Delta Lake. • Optimization & Analytics: Set up and manage Databricks SQL Warehouses, perform query optimization, and utilize internal visualizations for rapid data exploration. • Cloud Infrastructure: Leverage AWS cloud services to manage data storage, compute resources, and secure data movement. • DBA & Performance Tuning: Act as a custodian for our data platform—managing indexing, clustering, vacuuming, and performance tuning across both Delta Lake and relational/warehouse environments to ensure cost-efficiency and high speed. • Data Modeling: Design data models (Star Schema / Snowflake) in the Gold layer to ensure optimal performance for BI tools (e.g., Power BI, Tableau). • Data Governance: Manage metadata, permissions, and lineage using Unity Catalog and platform-specific access controls. • Quality Control: Implement automated Data Quality tests as an integral part of the CI/CD data pipelines. Requirements • 5+ years of experience as a Data Engineer or Data Engineer/DBA – Must. • Strong hands-on experience with the AWS ecosystem (S3, IAM, EC2, etc.) and modern cloud data warehouses, specifically Snowflake. • At least 1 year of intensive, hands-on experience with the Databricks platform (including Notebooks and Workflows). • High proficiency in PySpark (or Spark Scala). • Expertise in writing complex SQL (Window Functions, CTEs) alongside DBA-level performance tuning (query profiling, indexing, partition pruning). • Technical Knowledge: Practical experience with Delta Lake, file formats (Parquet), and working with Cloud Storage (AWS S3 / Azure ADLS). • Modeling: Proven experience in designing Fact and Dimension tables. Advantages (Nice to Have) • Experience with Delta Live Tables (DLT) or dbt (Data Build Tool) on Databricks or Snowflake. • Familiarity with MLOps concepts and MLflow. • Official AWS, Snowflake, or Databricks Certifications. • Experience with external orchestration tools (e.g., Apache Airflow). Soft Skills • Ability to work independently and lead tasks end-to-end in an Agile environment. • Strong troubleshooting skills for complex, distributed processes and database bottlenecks. • Excellent communication skills and the ability to collaborate with various stakeholders (Analysts, Product Managers, and Data Scientists). AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Summary Integration into the development team for a core DWH project and various AI projects. Responsibilities include the ingestion of large volumes of new data, followed by deep understanding and analysis of data in close collaboration with Data Scientists. Design, development, and optimization of critical, diverse, and large-scale data pipelines – in both cloud and on-premise environments. Key Responsibilities: • Take responsibility for the ingestion of large volumes of new data. • Conduct deep understanding and examination of data in close collaboration with Data Scientists. • Design and develop critical, diverse, and large-scale data pipelines and processes. • Deploy and optimize data solutions across both cloud and on-premise environments. Requirements • B.Sc. in Computer Science, Engineering, Mathematics, or Statistics - Mandatory. • 3+ years of experience as a Data Engineer. • 3+ years of development experience in Python - Mandatory. • Hands-on experience with Object-Oriented Programming (OOP). • Hands-on experience with Spark for large-scale data processing - Mandatory. • Hands-on experience with dbt. • Experience building and maintaining data pipelines using tools like Airflow or Kubeflow. • Practical experience and understanding of Docker, and container platforms such as ECS / Kubernetes. • Understanding of optimization techniques and working with advanced data formats, emphasizing Parquet and Iceberg (as well as Avro, HDF5, Delta Lake). • Hands-on experience with AWS data services (including Athena, Glue, EMR / EMR Serverless, Redshift, RDS) - Significant advantage. • Experience working with and integrating BI tools such as QuickSight, Tableau, and PowerBI. • Understanding of Machine Learning concepts and processes. • Practical knowledge of GenAI / prompt engineering solutions - Advantage. • initiative and independence, team player. • Self-learning ability, high analytical skills. • Ability to work under pressure, high level of responsibility and commitment. • Working Interfaces: Development team, production team, end-users. Why work for us? Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development. We offer competitive salaries, bonus incentives, benefits, flexible hours, and mentoring. Apply now to become part of the team. AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

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

Join one of Israel's leading insurance and financial companies as a Data Engineer on the core data team, building end-to-end Data solutions on a modern cloud stack. You'll work at the center of an enterprise-scale data landscape - Databricks, Spark, and Azure - with direct impact on BI, AI, and analytics capabilities across the business. What You'll Do: Develop and implement data pipelines and ETL/ELT processes, both Batch and Streaming. Build Data Products and enterprise-wide data solutions on Databricks, Spark, and Azure. Implement Data Quality, Monitoring, and CI/CD practices across data workflows. Work directly with business stakeholders and the BI, AI, and analytics teams. Own solutions from the characterization phase through to production. Requirements: 2+ years of experience as a Data Engineer - Must Strong hands-on experience with SQL and Python - Must Experience working with Databricks and Spark - Must Experience developing ETL/ELT processes in a Cloud environment - Must Experience working with Data Lake and Data Warehouse environments - Must Familiarity with Git, CI/CD, and DataOps practices - Must Ability to independently lead technical tasks and projects - Must Experience working in Enterprise environments - Advantage Familiarity with Data Governance and Data Quality practices - Advantage Experience supporting AI and Analytics projects - Advantage

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.

Description In your role as a Data Engineer at Tikal, you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies. Responsibilities: • Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT. • Develop Data pipelines to ingest and transform data. • Manage and enhance orchestrated pipelines across company • Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data. • Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads. • Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking. • Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage. Requirements • 7 years of experience in Python (+Java / Scala) • Proven experience working in an AI-assisted development environment, defining high-level technical goals and leveraging autonomous AI agents (e.g., Cursor, Copilot, Devin-style tools) to execute multi-file implementations, while maintaining ownership of architecture, design decisions, code quality, and final validation. • Experience in Building data lakes, including ingestion and transformation (DBT) • Data Warehousing & ETL Pipelines • SQL & Advanced Query Optimization • Experience in data formats (Parquet, Avro, ORC) • Experience in streaming (Spark Streaming, Flink, Kafka Streams, Beam, etc.) • Experience in NoSQL and data storage (e.g. Elastic, Redis, MongoDB, CouchBase, BigQuery, Snowflake, Databricks) • Experience in messaging (Kafka, RabbitMQ, etc.) • Experience in cloud platforms (AWS, GCP)

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.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Summary Integration into the development team for a core DWH project and various AI projects. Responsibilities include the ingestion of large volumes of new data, followed by deep understanding and analysis of data in close collaboration with Data Scientists. Design, development, and optimization of critical, diverse, and large-scale data pipelines – in both cloud and on-premise environments. Key Responsibilities: • Take responsibility for the ingestion of large volumes of new data. • Conduct deep understanding and examination of data in close collaboration with Data Scientists. • Design and develop critical, diverse, and large-scale data pipelines and processes. • Deploy and optimize data solutions across both cloud and on-premise environments. Requirements • B.Sc. in Computer Science, Engineering, Mathematics, or Statistics - Mandatory. • 3+ years of experience as a Data Engineer. • 3+ years of development experience in Python - Mandatory. • Hands-on experience with Object-Oriented Programming (OOP). • Hands-on experience with Spark for large-scale data processing - Mandatory. • Hands-on experience with dbt. • Experience building and maintaining data pipelines using tools like Airflow or Kubeflow. • Practical experience and understanding of Docker, and container platforms such as ECS / Kubernetes. • Understanding of optimization techniques and working with advanced data formats, emphasizing Parquet and Iceberg (as well as Avro, HDF5, Delta Lake). • Hands-on experience with AWS data services (including Athena, Glue, EMR / EMR Serverless, Redshift, RDS) - Significant advantage. • Experience working with and integrating BI tools such as QuickSight, Tableau, and PowerBI. • Understanding of Machine Learning concepts and processes. • Practical knowledge of GenAI / prompt engineering solutions - Advantage. • initiative and independence, team player. • Self-learning ability, high analytical skills. • Ability to work under pressure, high level of responsibility and commitment. • Working Interfaces: Development team, production team, end-users. Why work for us? Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development. We offer competitive salaries, bonus incentives, benefits, flexible hours, and mentoring. Apply now to become part of the team. AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description About AllCloud AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation. AllCloud is a leader in AI-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth. AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io Job Description We are looking for a talented and experienced Mid-Level Data Engineer with a passion for data and cloud technologies to join our team. In this role, you will be a key player in designing, developing, and maintaining our AWS and Databricks-based data platform. You will bridge the gap between robust Data Engineering, Database Administration (DBA), and high-performance Analytics Engineering. The ideal candidate understands the Lakehouse architecture, has experience managing modern cloud data warehouses like Snowflake, and possesses the ability to build scalable ETL pipelines that drive data-driven business decisions. Key Responsibilities • End-to-End Pipelines: Develop and maintain complex ETL/ELT pipelines using Python/PySpark on the Databricks platform, as well as loading and transforming data within Snowflake. • Data Architecture: Implement and manage data layers following the Medallion architecture (Bronze, Silver, Gold) using Delta Lake. • Optimization & Analytics: Set up and manage Databricks SQL Warehouses, perform query optimization, and utilize internal visualizations for rapid data exploration. • Cloud Infrastructure: Leverage AWS cloud services to manage data storage, compute resources, and secure data movement. • DBA & Performance Tuning: Act as a custodian for our data platform—managing indexing, clustering, vacuuming, and performance tuning across both Delta Lake and relational/warehouse environments to ensure cost-efficiency and high speed. • Data Modeling: Design data models (Star Schema / Snowflake) in the Gold layer to ensure optimal performance for BI tools (e.g., Power BI, Tableau). • Data Governance: Manage metadata, permissions, and lineage using Unity Catalog and platform-specific access controls. • Quality Control: Implement automated Data Quality tests as an integral part of the CI/CD data pipelines. Requirements • 5+ years of experience as a Data Engineer or Data Engineer/DBA – Must. • Strong hands-on experience with the AWS ecosystem (S3, IAM, EC2, etc.) and modern cloud data warehouses, specifically Snowflake. • At least 1 year of intensive, hands-on experience with the Databricks platform (including Notebooks and Workflows). • High proficiency in PySpark (or Spark Scala). • Expertise in writing complex SQL (Window Functions, CTEs) alongside DBA-level performance tuning (query profiling, indexing, partition pruning). • Technical Knowledge: Practical experience with Delta Lake, file formats (Parquet), and working with Cloud Storage (AWS S3 / Azure ADLS). • Modeling: Proven experience in designing Fact and Dimension tables. Advantages (Nice to Have) • Experience with Delta Live Tables (DLT) or dbt (Data Build Tool) on Databricks or Snowflake. • Familiarity with MLOps concepts and MLflow. • Official AWS, Snowflake, or Databricks Certifications. • Experience with external orchestration tools (e.g., Apache Airflow). Soft Skills • Ability to work independently and lead tasks end-to-end in an Agile environment. • Strong troubleshooting skills for complex, distributed processes and database bottlenecks. • Excellent communication skills and the ability to collaborate with various stakeholders (Analysts, Product Managers, and Data Scientists). AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.

Description Upstream delivers a cloud-based, AI-powered behavioral intelligence platform for physical AI and smart mobility. Upstream empowers customers with advanced AI-driven cybersecurity solutions, including detection and response (XDR), AI Security, quality and operational intelligence, cyber threat intel, SOC services, and more. Upstream is looking for an experienced Platform Engineer to join our core platform engineering team, focusing on developing and maintaining our AI-powered automotive data management platform. As a Platform Engineer, you will design, build, and operate scalable, production-grade data and ML platform components, ensuring high reliability and performance. You will work closely with our Data Engineering and Data Science teams to develop and enhance our Iceberg-based data platform, including a data lake, query engine, and MLOps tools, providing a solid AI-ready foundation for our automotive solutions. These solutions support millions of connected vehicles worldwide, focusing on cybersecurity (XDR), vehicle quality, and operational intelligence. You will work with technologies such as Iceberg, Trino, Prefect, GitHub Actions, Kubernetes, JupyterHub, MLflow, and dbt. This role is full-time and is Herzliya, Israel-based. Responsibilities • Design, build, and maintain scalable data pipelines to ingest and transform batch data on our data lake, enabling analytics and ML with strong data quality, governance, observability, and CI/CD. • Build and expand our foundational data infrastructure, including our data lake, analytics engine, and batch processing frameworks. • Create robust infrastructure to enable automated pipelines that will ingest and process data into our analytical platforms, leveraging open-source, cloud-agnostic frameworks and toolsets. • Develop and maintain our data lake layouts and architectures for efficient data access and advanced analytics. • Develop and manage orchestration tools, governance tools, data discovery tools, and more. • Work with other team members of the engineering group, including data architects, data analysts, and data scientists, to provide solutions using a use case-based approach that drives the construction of technical data flows. • End-to-end ownership mindset with a proactive, production-first approach Requirements • BSc/BA in Computer Science, Engineering or a related field • At least 4 years of experience with designing and building data pipelines, analytical tools and data lakes • Experience with the data engineering tech stack: ETL & orchestration tools (e.g. Airflow, Argo, Prefect), and distributed data processing tools (e.g Spark, Kafka, Presto) • Experience with Python is a must • Experience working with open-source products - Advantage • Experience working in a containerized environment (e.g. k8s) - Advantage • Development experience, using a general purpose programming language (Java, Scala, Kotlin, Go, etc.) - Advantage Upstream is an equal opportunity employer. All candidates for employment will be considered without regard to race, color, religion, sex, national origin, physical or mental disability, veteran status, or any other basis protected by applicable federal, state or local law.

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.