Top Data Engineer Jobs in Israel

Design and build scalable data pipelines and models that normalize and correlate security data for Torq's AI-SOC context graph. Collaborate with product and stakeholders to run end-to-end POCs, profile and validate datasets, integrate identity/endpoint/security sources, and deliver production-ready dbt-backed models. Measure coverage and accuracy, and iterate on data models to enable autonomous security decisions.

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.

The Data Engineer will create and maintain data pipelines, implement high-scale systems, collaborate with engineers and data scientists, and leverage AI/ML technologies to fulfill customer requirements.

As a Data Engineer, you will design and maintain data pipelines, develop APIs, integrate LLMs and ML models, and optimize performance for scalability and reliability.

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.

Design, build, and maintain scalable data pipelines, ETL processes, and data infrastructure. Optimize storage and retrieval for structured and unstructured data, integrate data solutions with products, apply DevOps practices (CI/CD, IaC, observability), and use AWS and ElasticSearch. Maintain SQL and NoSQL databases and leverage containerization (Docker, Kubernetes).

The Data Engineer will design and develop data solutions for clients, build data pipelines, lakes, DWHs, and implement AI/ML models.

Design, build, and operate production Snowflake data pipelines and models (ingestion, CDC, transformation, validation). Create analytics-ready, governed datasets and semantic models for BI and enterprise AI, enforce security/governance and FinOps, and partner with stakeholders on certified metrics, lineage, and delivery.

The Data Engineer will design and maintain data pipelines, develop data platforms, ensure data quality, and troubleshoot issues.

Design and own the agentic data infrastructure: build high-availability pipelines (GCP/BigQuery/Postgres), semantic layer, RAG/ML backbone, crawlers/scrapers, and operationalize predictive models for AI agents.

Lead architecture and schema design for a new data product, build full-stack production features, design and optimize data pipelines, integrate complex systems, manage cost and performance, mentor engineers, and collaborate with cross-functional teams to deliver scalable data solutions.

Develop end-to-end features and systems for WalkMe's Big Data Platform, design core components and microservices, implement best practices, and introduce cutting-edge technologies to scale AI-ready data infrastructure and real-time context streaming.

Design and improve ML-driven data curation pipelines for large autonomous-vehicle datasets: embedding extraction, VLM scene analysis, scoring, deduplication, sampling, GPU inference at scale, validation/diagnostics, dataset SDK and 3D geometry tooling, and collaborate with algorithm and infra teams to close data gaps.

Build and improve ML-driven data curation pipelines for large-scale autonomous-vehicle datasets: embedding extraction, scene detection, GPU inference orchestration, scoring, deduplication, sampling, dataset validation, SDK/tools, and collaboration with algorithm and infra teams to ensure balanced, high-quality training data.

Build and improve ML-driven data curation pipelines for autonomous-vehicle datasets: run GPU inference at scale, extract embeddings, score/deduplicate/sample data, ensure rare scenarios are represented, validate dataset quality, and contribute to dataset SDKs and 3D geometry tooling while collaborating with algorithm and infra teams.

Design and implement the decision-making logic of a Price Optimizer: translate pricing rules into mathematical models and production-ready Python code, refine simulation and optimization algorithms, write tested maintainable code, validate model behavior with data, and collaborate with Solution Architects and Data Engineers to integrate models into Dagster pipelines.

Design and implement pricing decision logic and optimization models in production-quality Python. Translate business rules into mathematical constraints, validate model behavior with data, collaborate with Solution Architects and Data Engineers to integrate models into Dagster pipelines.

Design, build, and operate Flink-based streaming pipelines and real-time processing systems at production scale. Own data quality, schema contracts, observability, and alerting. Build knowledge-base tooling for agent/engineer access. Drive data integrity and resilient recovery to keep the platform trustworthy.

Develop and own metric-extraction, measurement, and telemetry tools for real-time data collection on NVIDIA hardware. Collaborate with applied researchers, architects, and data engineers to iterate on ML diagnostic and predictive-maintenance solutions, and translate data insights to stakeholders.

Build and maintain distributed data pipelines (SQS, Airflow, Kubernetes), integrate ML outputs into ETL workflows, contribute to shared Python libraries, debug multi-service pipelines, improve observability, and manage ETL/data infrastructure and database operations for large-scale datasets.

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.

The Software Engineer will handle data collection for AI, extending the ingestion cloud infrastructure on GCP, integrating data processing workflows, and collaborating on dataset roadmaps.

The Senior Data Engineer will manage big data pipelines, ensuring robust architecture and data integrity, while leading complex projects in a fast-paced environment.

Design and maintain scalable data pipelines, implement Lakehouse architecture, develop ETL processes, and ensure data integrity and quality.

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.