Top Data Engineer Jobs in Jerusalem

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 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.

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.

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

Designs, develops, deploys, and optimizes large-scale data pipelines across cloud and on-premise environments. Ingests high-volume data, analyzes datasets with Data Scientists, and builds data solutions for data warehouse and AI projects. The role requires Python, Spark, dbt, pipeline orchestration, container platforms, advanced data formats, AWS data services, and BI tool integration, along with strong analytical, collaborative, and independent working skills.

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.

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 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.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

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.

Design, build, and operate scalable data platform infrastructure, including high-volume batch and streaming ingestion, data lake architecture, Kubernetes-based processing with KEDA, Terraform IaC, data orchestration (Airflow/Prefect), Snowflake usage, and performance tuning to support analytics and AI workloads.