Top Data Engineer Jobs in Jerusalem

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.

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.

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 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.

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

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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.

Test and approve ETL processes, database scripts, and data pipelines for accuracy, integrity, performance, and reliability. Build automated testing scripts and simulations, apply BDD methodologies, create test documentation and feature files, troubleshoot defects, perform root cause analysis, and support production maintenance. Collaborate with development and data teams to improve testing processes and data quality.

Build and manage scalable data lakes, warehouses, ETL pipelines, and orchestrated ingestion and transformation workflows. Develop data models, optimize SQL queries, support streaming and high-volume architectures, and implement data security, governance, quality, compliance, and lineage. The role requires ownership of architecture and code quality while using AI-assisted development tools to deliver complex multi-file implementations.

Design, develop, and maintain AWS- and Databricks-based data platforms and ETL/ELT pipelines using Python, PySpark, Snowflake, and Delta Lake. Manage Medallion architecture, cloud infrastructure, data warehouses, performance tuning, data modeling, governance, metadata, permissions, lineage, and automated data quality testing. Collaborate with analysts, product managers, and data scientists while troubleshooting distributed systems and database bottlenecks.

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.

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.