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.

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.

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.

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.

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.

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.