Top Data Engineer Jobs in Jerusalem

Seeking a Data Engineer with over 4 years of experience to build and optimize data platforms, work on data pipelines, and collaborate across teams to enhance data solutions and architecture.

Design, build, and maintain scalable ETL/data pipelines and storage for large datasets. Collaborate with product, data science, and analysts to ensure data quality, integrity, and low-latency performance. Implement monitoring, validation, and cost-efficient solutions, recommend new tools, and leverage AI assistants to improve productivity and pipeline development.

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

The role involves developing and maintaining data streaming processes, enhancing real-time data capabilities, and supporting production systems in a collaborative team environment.

Design, build, and maintain scalable ETL/ELT data pipelines and data warehouse solutions. Integrate and model data from multiple sources, ensure data quality and monitoring, and support production deployments. Develop and operationalize AI/ML and LLM-powered data solutions, collaborate with global stakeholders, and provide production support and anomaly detection capabilities.

Build and maintain complex ETL pipelines and backend BI/data solutions using SSIS, Airflow, dbt and Snowflake. Work with high-scale databases, design implementations, and collaborate with stakeholders across the company.

Build and maintain Trigo's analytics data stack: design analytics-ready data models, own the BI semantic layer, ensure data quality and observability, collaborate with stakeholders to translate business questions into scalable data solutions, and support ad-hoc analyses.

Lead end-to-end manual QA for data-driven systems and ETL pipelines: web/API testing, complex SQL validation across DWH flows, log analysis, test planning/execution, defect reporting, and using AI tools to generate tests and optimize testing processes.

Own, operate, and scale real-time data platforms (Kafka, Elasticsearch). Build automation, IaC, monitoring, internal tools and self-service APIs. Troubleshoot production incidents, run RCA, perform capacity planning, and collaborate with engineers and analysts to ensure reliable data delivery.

Own and evolve ClickHouse's Python connector and SDK ecosystem; build enterprise-grade integrations with orchestration and transformation tools; design AI/LLM integration patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate with Product and Cloud teams to prioritize and deliver robust, high-performance connectors.

Build and prototype AI-driven cybersecurity product features, develop AI/data infrastructure and pipelines, analyze product and security data, define KPIs and alerts, and provide insights to Product, R&D, and GTM teams.

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.

The Senior Data Engineer will design and implement Navina's data platform, transform and serve medical data, and collaborate with cross-functional teams.

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.

The Staff Data Engineer will design and maintain data models, pipelines, and ETLs, ensuring data correctness and collaboration with cross-functional teams to meet business needs.

The Senior Data Engineer will design, build, and maintain data infrastructure for analyzing large datasets, focusing on data management, ETL processes, and collaboration with stakeholders to align data projects with business objectives.