Top Data Engineer Jobs in Israel

Develop and maintain end-to-end features and systems for WalkMe's Big Data Platform. Act as a technical focal point for best practices, design components and services, and introduce cutting-edge technologies to scale AI-ready data layers and real-time context streaming.

Own and scale AWS OpenSearch infrastructure and PostgreSQL-to-OpenSearch pipelines. Design indices, mappings, analyzers, and retention policies. Optimize queries and bulk indexing, monitor cluster health, implement retries/idempotency, backfills, reconciliation, and disaster recovery. Build dashboards, alerts, runbooks, and participate in incident response while collaborating with backend, DB, infra, and product teams.

As a Software Engineer for Data Infrastructure & Acquisition, you will manage data collection processes, optimize data ingestion pipelines, and collaborate with AI scientists to enhance data quality for model training.

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.

Develop backend services using Rust and other technologies for a cloud-native platform that processes large volumes of data daily.

Design, build, and scale batch and streaming data pipelines, warehouses, and cloud data platforms. Collaborate with researchers and ML teams to deliver reliable, optimized datasets. Lead migrations/backfills, implement event-driven architectures, operate distributed processing engines, and deploy data infrastructure in Kubernetes for high availability and performance.

The Data Science Engineer will develop data collection strategies, create and deploy data analysis scripts, and ensure data quality while leveraging AI for predictive insights.

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 and implement high-performance, low-latency runtime enforcement for data access across databases and sensitive data. Build proxies, orchestrators, and production-grade runtime components; ensure high availability, observability, and safe upgrades. Participate in design tradeoffs across security, performance, scalability, and operability while collaborating with product, platform, and security teams.

Build and optimize distributed data and evaluation pipelines at petabyte scale for deep-learning training. Create tools for data exploration, validation, and model packaging; improve memory use, throughput, and iteration time. Coordinate data contracts and workflows with researchers, infrastructure teams, and internal stakeholders, and integrate with downstream deployment and EyeQ hardware.

Design, develop, deploy, and maintain agents and threat-detection algorithms across the full data pipeline. Collaborate with backend and DevOps teams, operate across major cloud providers, and work with Kubernetes, OpenSearch, and Redis.

Design and develop robust data pipelines and integrations for financial applications, ensuring high availability and data governance.

Design and implement core YTsaurus functionality in C++, build and evolve platform-level capabilities, improve APIs and UX for internal users, own production quality including incident response/on-call, and collaborate with stakeholders to prioritize and deliver scalable data platform features.

Design and implement scalable data pipelines and microservices, maintain backend services, and evolve the platform infrastructure for effective data management.

The Senior Data Engineer will design and maintain data infrastructure and pipelines, ensuring high-quality data for analytics and AI/ML workflows.

Lead design and development of scalable ML inference and large-scale data processing systems. Optimize inference algorithms and pipelines, build high-throughput streaming and preprocessing components, improve distributed system performance and reliability, and drive architecture with ML researchers and infrastructure teams for production workloads.

Design, build, and optimize ETL/ELT data pipelines with Apache Spark; manage cloud data infrastructure (GCP/AWS) and warehouse/lake solutions; collaborate with product, data scientists, and engineers; implement monitoring, alerting, and logging to ensure data quality and availability for analytics and ML.

Design, build, and maintain Lemonade's data foundation: ingestion layers, streaming and batch pipelines, CDC, data lake, and scheduling infrastructure. Ensure data quality, governance, and enable data-mesh practices while partnering with engineers, ML teams, data scientists, and product managers.

As a Big Data Engineer, you will design and implement high-scale systems for data retrieval and analysis, working alongside data engineers, data scientists, and analysts. Responsibilities include improving infrastructure, experimenting with technologies, and monitoring data pipelines.

The role involves building scalable data pipelines, designing infrastructures using cutting-edge technologies, and collaborating with teams to solve customer data problems. Responsibilities include deploying hybrid environments, tuning storage backends, and creating documentation to support best practices.

The Senior Java Backend Developer will design and develop data systems for processing security events, ensuring high availability and scalability in a cloud-native environment.

Lead technical vision and architecture for the Data Platform across Data Engineering Infra, ML Platform, and Data Warehouse teams. Design and implement data lakes/warehouses, batch and streaming pipelines, ensure data quality and performance, build ML infrastructure (feature stores, real-time models, PIT retrieval), collaborate with cross-functional teams, mentor engineers, and uphold engineering standards.

Design, develop, and maintain scalable backend services and internal tools for the data platform; contribute to platform architecture; collaborate with security researchers and product teams to turn cybersecurity ideas into production-ready software; apply engineering best practices for testing, observability, CI/CD, and maintainability.

The role involves collaborating with teams to develop AI solutions, improve data quality, and enhance engineering productivity through automated tools and workflows.

Design, build, and operate Flink-based streaming pipelines and real-time data systems. Own data quality, observability, schema contracts, and knowledge-base tooling. Drive data integrity, recovery, and platform reliability while setting architecture direction and collaborating on company-wide data initiatives.