Top Data Engineer Jobs in Israel

Lead the technical direction of a large-scale data platform supporting reporting, billing, machine learning, experimentation, and business decisions. Design and operate distributed systems using Spark, Iceberg, Kafka, and StarRocks; improve reliability, performance, cost, and observability; mentor engineers; guide architecture and roadmaps; and remain hands-on with production code, prototypes, incident response, and cross-functional technical leadership.

Designs and develops data protection system extensions and services for macOS security sensors. Owns features from design through delivery, collaborates with distributed cross-functional teams, and troubleshoots customer-reported product issues. The role requires strong C++ and/or Objective-C development skills, experience with macOS debugging tools and frameworks, and the ability to apply AI technologies to improve workflows and decision-making.

Design, build, and maintain scalable data pipelines, ETL/ELT processes, analytics data models, and warehouse infrastructure. Collaborate with analysts, data scientists, engineers, and product managers to deliver reliable, actionable data solutions. Champion data quality, governance, and system reliability while troubleshooting issues, optimizing performance, mentoring teammates, and using AI coding assistants to support development and incident triage.

Designs, develops, deploys, and optimizes large-scale data pipelines for data warehouse and AI projects. Responsibilities include ingesting high-volume data, analyzing data with Data Scientists, and implementing solutions across cloud and on-premise environments using Python, Spark, dbt, AWS services, and container platforms.

Design, build, and operate scalable data pipelines, data lakes, analytics engines, orchestration systems, and ML platform infrastructure. Ensure data quality, governance, observability, reliability, and CI/CD across batch-processing workflows. Collaborate with data architects, analysts, and data scientists to develop production-grade, cloud-agnostic data solutions supporting AI-powered automotive cybersecurity, quality, and operational intelligence platforms.

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.

Develop and optimize distributed data-processing and model-evaluation pipelines for close-range 3D perception systems in autonomous vehicles. Build tools for image, video, and geometric data exploration, validation, manipulation, and quality assessment. Collaborate with deep learning researchers and infrastructure teams to improve data workflows, computational efficiency, model performance, and training cycles.

Build and operate a Spark-on-Kubernetes lakehouse platform (Iceberg + AWS Glue), develop frameworks and CI/CD for data pipelines, run Airflow orchestration, design batch/streaming and Kafka-based flows, manage cost/performance trade-offs, and support platform reliability and developer enablement.

Build and optimize distributed data, evaluation, and ML pipelines for 3D perception models in autonomous driving. Develop tools for image, video, geometric, and model-output processing; improve memory efficiency, throughput, and computation costs. Own model packaging, distribution, and deployment interfaces, while coordinating data contracts and workflows across research, infrastructure, data, and internal customer teams.

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.

Develop production algorithmic systems that transform inconsistent documents, drawings, imagery, and other data into reliable geospatial intelligence. Responsibilities include problem definition, data collection and processing, relevance and reliability assessment, document and image understanding, coordinate anchoring, evaluation methodology, and production deployment. The role applies computer vision, geometry, classical algorithms, machine learning, and LLM-based approaches while collaborating with mapping experts, surveyors, and researchers.

Build and operate high-scale data pipelines, streaming systems, and data products from design through production. Develop platform infrastructure, optimize performance, ensure data quality, and provide production support. Research technologies and integrations, collaborate with product and engineering stakeholders, leverage AI capabilities, and mentor developers through complex projects.

Own and expand DoorLoop’s data platform across modeling, ingestion, semantic layers, reliability, and financial reconciliation. Partner with business leaders to define metrics, build dashboards and AI data products, and establish engineering standards. Lead dbt development, warehouse optimization, monitoring, incident response, code review, and analyst mentorship. The role requires strong SQL, Python, Snowflake, dbt, pipeline engineering, stakeholder communication, and practical experience building trustworthy AI-enabled data products.

Build and operate data and ML infrastructure for multimodal sensor data, supporting customer algorithm teams across annotation, training, evaluation, deployment, and observability. Develop pipelines, data environments, model onboarding, and evaluation workflows; work directly with customers to solve technical needs and translate insights into platform improvements. Seed data products and collaborate with algorithms, product, software, and DevOps teams to establish company-wide data and MLOps standards.

Designs and maintains backend services, production data pipelines, batch and event-driven ETL processes, data models, schemas, integrations, and database systems. Owns production monitoring, observability, security, troubleshooting, and incident handling. Collaborates with engineering, DevOps, data, and implementation teams while using cloud technologies, Kubernetes, and AI-assisted development tools to deliver scalable software solutions.

Design, develop, test, debug, and productionize data-plane features for large-scale distributed networking systems using C/C++. Investigate complex multi-component issues, collaborate with engineering, QA, and product teams, and integrate AI tools into daily development workflows. The role involves working with ASIC architectures and chip SDKs to implement networking features such as forwarding, quality of service, security, and telemetry.

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.

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.

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.

Designs and maintains scalable data pipelines, ETL/ELT workflows, analytics platforms, and data infrastructure. Collaborates with Product, BI, Sales, and other teams to deliver integrated data solutions. Owns the data lifecycle, implements quality checks and governance, monitors performance, resolves data issues, and applies GenAI and AI-assisted tools to improve engineering productivity and automation.

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.

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.

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.