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.

Build and productionize LLM-powered and machine-learning pipelines for product classification, normalization, taxonomy mapping, and entity matching across billions of records. Develop agentic workflows, evaluation frameworks, and scalable Spark/Databricks data solutions. Balance accuracy, cost, and latency while collaborating with Product, data engineering, data science, and R&D teams to move solutions from proof of concept into production.

Lead the design and development of scalable data pipelines integrating financial, operational, and leasing data into JLL’s Asset Beacon platform. Maintain platform reliability, security, and scalability; collaborate with developers and stakeholders; mentor junior engineers; and conduct code reviews. Use Spark, Airflow, Kubernetes, Azure, and related data technologies while applying strong Python, PySpark, SQL, data modeling, and data warehousing expertise.

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.

Design, develop, and manage large-scale online and offline data ingestion pipelines and infrastructure. Ensure high availability, reliability, scalability, system health, and data quality across production environments processing billions of daily events. Lead technical design decisions, conduct code reviews, solve performance and business-logic challenges, collaborate with product and engineering leaders, and mentor team members.

Designs data architecture, pipelines, governance, and quality standards while developing and deploying production ML models. Owns the full model lifecycle from research and experimentation through monitoring and iteration. Partners with Product and R&D to deliver AI capabilities for vulnerability detection, attack emulation, and security insights, while evaluating build, buy, and fine-tuning options.

Design and implement the decision-making logic for a price optimization engine. Translate pricing strategies and business rules into mathematical models and production-quality Python code, refine simulation and revenue management algorithms, validate model behavior, identify edge cases, and collaborate with solution architects and data engineers on pipeline integration.

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.

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, and deploy cloud-native, high-scale backend and Big Data systems for security intelligence. Optimize data pipelines, integrate machine learning models into production, and advance AI initiatives from proof of concept to production features. Collaborate with product, UX, data science, and engineering teams to build tools that detect and address cyber threats. The role requires backend or data engineering experience, distributed systems expertise, cloud proficiency, and practical knowledge of machine learning, LLMs, agents, and generative AI.

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.

Own the technical direction of scalable data pipelines processing billions of web events. Design, build, and operate end-to-end systems from ingestion through distributed processing and serving. Lead architecture, write design documentation, make cost-latency-correctness trade-offs, guide technical planning, resolve production incidents, and ensure data quality. The role requires deep Apache Spark, cloud data warehouse, orchestration, programming, and large-scale production engineering expertise.

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.

Build and maintain high-scale data platforms, backend services, APIs, event-driven systems, data pipelines, integrations, and LLM-powered features. Own multiple systems from ambiguous requirements through production, ensuring reliability, data quality, and appropriate cost, latency, and performance. Work across Azure, GCP, and AWS while developing internal tools that support company-wide data-driven decision-making.

Develop end-to-end features and systems for WalkMe’s Big Data Platform, build and design distributed services and microservices, establish software development best practices, and introduce innovative technologies. The role supports high-performance data infrastructure, real-time context streaming, AI-ready data layers, dashboards, APIs, and enterprise workflow automation.

Design, build, and own reliable production data pipelines for analytics and machine learning using Python and SQL. Implement idempotent data processing, transformations, validation, quality checks, and performance optimization. Orchestrate workloads with Airflow or equivalent frameworks, and run them using Docker and Kubernetes. Collaborate with Analytics, Data Science, and ML teams while contributing technical leadership, CI/CD automation, cloud operations, and cost-efficient infrastructure management.

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.

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.