Top Data Engineer Jobs in Israel

Design and implement scalable data pipelines and microservices, maintain backend services, and evolve the platform infrastructure for effective data management.

Design and develop robust data pipelines and integrations for financial applications, ensuring high availability and data governance.

Own Guidde’s internal product suite, including full-stack applications, dashboards, AI agent workflows, automation pipelines, and custom APIs. Build data-driven tools for Product, Customer Success, Sales, and executives; transform data models into usable interfaces and automated actions; and manage deployment, containerization, observability, and CI/CD. The role provides technical leadership, makes architectural decisions, partners with data teams, and translates business challenges into reliable software.

Lead architecture and development of backend microservices powering distributed ETL infrastructure. Build and scale high-throughput batch and real-time data pipelines, optimize performance and reliability, own services through deployment and observability, and collaborate with product, data science, analytics, and R&D teams. Mentor engineers and contribute to system design, API development, testing, and operational improvements.

Build and maintain scalable cloud-based data pipelines and ETL processes using Python, SQL, AWS, Airflow, Databricks, and Spark. Design data models, optimize large-scale workloads, integrate APIs and databases, and monitor pipeline reliability. Develop AI agents with LangGraph and related frameworks that connect tools and data sources and automate user workflows. Collaborate with BI analysts and stakeholders to define data requirements and resolve data issues.

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.

Build and maintain automated data validation for large-scale event pipelines. Validate end-to-end data flows, schemas, freshness, latency, anomalies, and dashboard accuracy using SQL and automation. Investigate discrepancies across production systems, warehouses, object storage, and staging environments. Create CI/CD validation jobs, reports, dashboards, and monitoring processes while collaborating with engineering, data, QA, and product teams to resolve data quality issues and support scalability testing.

As a Big Data Engineer, you will design and implement high-scale systems for data retrieval and analysis, working alongside data engineers, data scientists, and analysts. Responsibilities include improving infrastructure, experimenting with technologies, and monitoring data pipelines.

The role involves building scalable data pipelines, designing infrastructures using cutting-edge technologies, and collaborating with teams to solve customer data problems. Responsibilities include deploying hybrid environments, tuning storage backends, and creating documentation to support best practices.

Build and maintain production backend services and data pipelines (batch & streaming) powering analytics, experimentation, and AI training. Own data modeling, quality, schema evolution, observability, and reliable access. Collaborate cross-functionally to define data contracts, orchestration, and operational reliability for Loora's data platform.

The role involves collaborating with teams to develop AI solutions, improve data quality, and enhance engineering productivity through automated tools and workflows.

Design, develop, and maintain scalable backend services and internal tools for the data platform; contribute to platform architecture; collaborate with security researchers and product teams to turn cybersecurity ideas into production-ready software; apply engineering best practices for testing, observability, CI/CD, and maintainability.

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.

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.

Design and implement core components of classification and policy stack, optimize data workloads, and partner on cross-functional initiatives for data governance.

Build and own the lakehouse analytical storage and batch processing at petabyte scale: data modeling, schema/table design, partitioning, retention, Spark/EMR batch jobs, Java/Spring Boot APIs, query optimization, file layout/compaction, and cross-team collaboration to ensure performance and cost efficiency.

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.

Seeking a Senior Software Engineer for data path development. Responsibilities include design, coding, integration, and debugging of C/C++ software. Contributions aid AI infrastructure.

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.

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