Top Data Engineer Jobs in Israel

Own end-to-end analytics foundations, including data models, pipelines, semantic layers, BI infrastructure, data quality, and observability. Partner with Data Science and business stakeholders to create trusted metrics supporting Product, GTM, and Finance. Mentor team members, establish engineering standards, lead cross-functional initiatives, and use GenAI to improve analytics development speed and accuracy. The role is hybrid in Tel Aviv with three office days weekly.

Lead the architecture and development of large-scale batch and streaming data pipelines supporting real-time pricing decisions. Own data lake design, table modeling, partitioning, performance, cloud costs, data quality, observability, and reliability across AWS and GCP. Partner with backend, product, and data science teams on APIs and AI-driven data products. Mentor engineers, review designs and code, establish technical direction, and remain hands-on with implementation and delivery.

Own and evolve Lusha’s shared data platform across Databricks, Kafka, CDC, Airflow, Elasticsearch, AWS, Kubernetes, and databases. Build and operate reliable batch and streaming pipelines, improve performance and cost, establish governance and observability, automate manual processes, and lead cross-team architecture initiatives. The role also requires daily use of AI coding tools and building LLM, agent, and MCP-based platform workflows.

Own the data engineering layer of a cloud and SaaS billing integrations platform. Build and maintain third-party API integrations, ingestion pipelines, normalization models, orchestration, backfills, reconciliation, and data-quality checks. Ensure accurate, complete, deduplicated financial data across vendors, marketplaces, currencies, and pricing models. Use AI throughout the engineering workflow and collaborate with product, support, and backend teams. The role requires production pipeline ownership, strong SQL and Python, cloud data warehouse experience, and expertise handling API and data reliability challenges.

Build and maintain the analytics data platform for Artifactory product analytics. Design product telemetry with product managers and engineers, develop production-grade dbt models in layered warehouse structures, standardize entities and definitions, and enable self-service access to trusted datasets. The role requires strong SQL, data modeling, cloud warehouse, event-tracking, Git, and AI-assisted coding experience, with close collaboration across analysts, PMs, and R&D.

Design, build, and maintain scalable ETL/ELT pipelines using Python, PySpark, Databricks, and Apache Spark. Integrate diverse data sources, optimize processing workflows for performance and cost, and develop cloud-based data solutions. Collaborate with software and multidisciplinary teams to define requirements, deliver end-to-end data solutions, and complete complex data engineering initiatives.

Own Venn’s data domain end to end by building scalable pipelines, reliable data models, dashboards, reporting layers, product analytics, and executive insights. Partner with teams across the company to translate business questions into actionable recommendations, establish data standards and quality checks, and identify opportunities for AI-supported analytics and customer-facing data products.

Design, build, and maintain end-to-end data pipelines and models. Integrate multiple data sources, improve data quality and reliability, automate workflows, and partner with Product, Finance, Operations, and R&D to deliver trusted datasets and analytics-ready data assets.

As a Senior Software Engineer, you'll develop CrowdStrike's browser protection solutions, focusing on data protection and secure browsing experiences, collaborating on features from design to implementation.

Build and operate Nebius’s data platform for search quality, ML pipelines, product analytics, and business operations. Responsibilities include developing batch and streaming pipelines, evolving warehouse models, improving observability and data quality, supporting production incident resolution and backfills, and partnering cross-functionally to deliver trustworthy datasets. The role covers ingestion, medallion-layer warehousing, consumer datasets, dashboards, governance, testing, documentation, deployment, and architectural standards across large-scale, multi-region systems.

Own the architecture and delivery of web-scale data ingestion pipelines, including resilient web scraping, transformation, storage, distributed cloud systems, data quality, APIs, and workflow monitoring. The role requires expertise in Airflow, Databricks, anti-bot scraping, scalable system design, AI coding assistants, and connecting pipeline decisions to business value. This position is based four days per week in Tastewise’s Tel Aviv office.

Design and develop scalable systems and tools for data operations, workflow management, pipeline monitoring, reporting, and operational processes. Collaborate with analysts, product stakeholders, and infrastructure teams to define requirements and deliver production systems. Transform manual workflows into maintainable solutions, troubleshoot data and infrastructure issues, and own projects from design through deployment. Development is primarily in Python, with opportunities to use containerized environments and CI/CD pipelines.

Design and develop scalable systems, workflow tools, data pipelines, monitoring solutions, and operational platforms. Collaborate with analysts, product stakeholders, and infrastructure teams to define requirements and deliver production systems. Transform manual processes into maintainable solutions, troubleshoot pipeline and infrastructure issues, and own projects from system design through deployment and continuous improvement, primarily using Python.

Develop and operate high-throughput distributed systems and microservices for data ingestion, processing, governance, retention, usage reporting, and multi-region routing. Build Kafka-based pipelines and efficient persistence using PostgreSQL, Redis, column-oriented formats, and object storage. Work with cloud-native systems written in Rust and Scala, deployed on Kubernetes across multiple cloud environments.

Design, build, and maintain scalable data warehouse infrastructure and pipelines. Enable analytics and product teams via data modeling, BI dashboards, data governance, and quality monitoring.

Design, build, and maintain data management systems and ETL pipelines, ensuring data quality and collaborating with teams for optimal solutions.

Lead Interconnect Cluster Performance efforts using big data and failure analysis to ensure high-quality product deliveries. Drive cross-functional collaborations (Process, Mechanical, Hardware, Thermal), lead task forces, design and improve test plans, manage field and lab failure analysis, and partner with Customer Support, Marketing, Engineering, Quality, and Reliability to improve product performance and customer satisfaction.

Designs, implements, and maintains scalable, highly available database and data-processing infrastructure for transactional, real-time, analytics, and big-data workloads. Builds CI/CD automation for infrastructure changes, migrations, and upgrades; collaborates with developers and security teams; applies security, compliance, disaster recovery, monitoring, and high-availability best practices across self-hosted and cloud environments.

Develop road perception models and algorithms using vision, maps, and multi-sensor data. Own end-to-end road data pipelines across cloud and on-premises environments, perform geometric reasoning from 2D images to 3D world space, implement algorithms for autonomous vehicle production systems, and build evaluation tools, metrics, and supporting infrastructure.

As a Backend/Data Engineer, you'll design scalable data solutions, optimize performance, and maintain data pipelines while ensuring data quality.

Designs, develops, deploys, and optimizes large-scale data pipelines across cloud and on-premise environments. Ingests high-volume data, analyzes datasets with Data Scientists, and builds data solutions for data warehouse and AI projects. The role requires Python, Spark, dbt, pipeline orchestration, container platforms, advanced data formats, AWS data services, and BI tool integration, along with strong analytical, collaborative, and independent working skills.

Design, develop, and maintain ETL/ELT pipelines, troubleshoot queries, implement data governance, and stay updated on data engineering technologies.

Lead end-to-end data and automation solutions for Clinical Research: intake, business case development, design, build, validation, deployment and lifecycle management of ETL pipelines, dashboards, workflow automation and AI-assisted processes. Ensure compliance (ICH GCP), measure impact via KPIs, manage stakeholders and change, and scale reusable solutions across countries and region.

Design, develop, test, maintain, and debug user-space embedded applications at low and system levels. Participate in architecture definition, design reviews, test planning, and software integration. Collaborate with software, integration, systems, support, and hardware teams while keeping current with IoT technologies and development tools.

Develop embedded software and control systems for high-performance power electronics in data centers. Responsibilities include designing, optimizing, testing, and maintaining real-time firmware across multiprocessor platforms; developing on microcontrollers; owning products from simulation through field deployment; and integrating software with hardware. The role requires expertise in C/C++, power conversion topologies, control algorithms, RTOS or bare-metal programming, embedded interfaces, and firmware debugging.