Top Data Engineer Jobs in Germany

Design and maintain scalable data pipelines, connectors, and reusable data products to make enterprise data AI-ready. Enable RAG, semantic search, agentic workflows, and production deployment of AI solutions while enforcing data quality, governance, metadata, security, and cross-team collaboration.

Support engineering team on machine learning projects: data processing, maintenance, reporting, troubleshooting. Analyze and optimize processes, suggest improvements, and learn industrial practices in a startup environment.

Design, build, and maintain data pipelines and ETL processes; ensure data quality and availability; collaborate with cross-functional teams to support analytics and reporting; implement data solutions and support data platform operations.

Design, build, and operate scalable batch and streaming data pipelines; optimize storage and query performance on a data lakehouse; implement data quality, lineage, security controls; build observability and automation; write well-tested production code and maintain documentation while collaborating across teams.

Design, build and maintain scalable data pipelines and BI infrastructure. Ensure secure, high-quality data availability through modelling, performance tuning, governance, and collaboration with analysts and stakeholders. Mentor peers, review code, and introduce new tools and best practices.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Own and evolve data storage and warehouse foundations (schemas, migrations, performance, reliability). Design data models and marts for analytics, build backend services and RESTful APIs in Python, improve testing, monitoring, and reliability, and collaborate with product and engineering teams to deliver data-driven features.

Design, build, and maintain scalable ETL pipelines and data models for robotics; ensure data quality, governance, and security; develop streaming and dataset curation workflows; integrate modern data technologies; collaborate across teams to deliver high-quality datasets.

Design, build, and operate scalable data systems and ETL pipelines for research and live trading. Transform raw data into reliable datasets, collaborate with researchers, manage storage systems, and maintain production observability and performance.

As a Data Engineer, you will design and build data pipelines, models, and infrastructure for analytics and machine learning while ensuring data quality and compliance.

We need an experienced data engineer to help our clients organize and analyze data for critical missions. You'll develop data pipelines and operationalize data applications in a fast-paced environment.

Design, train, test, deploy, and maintain machine learning models and pipelines to process large datasets for AFRICOM. Collaborate with other engineers to select ML algorithms, build mission-critical solutions, and provide technical guidance to senior military stakeholders.

The Data Engineer will manage tracking data pipelines, ensure data quality and governance, optimize ELT processes, and collaborate with teams to enhance data accessibility and reliability.

The role involves analyzing data for insights, creating dashboards, and collaborating with teams to optimize product performance using SQL, Python, and visualization tools.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Design, implement, scale, and operate Python APIs, backend services, and end-to-end data pipelines on cloud platforms. Build event-driven real-time/asynchronous processing, apply data modeling and database design, enforce CI/CD and testing, drive observability and performance, and own services from schema and API design to deployment and monitoring.

Develop, deploy and improve cloud-based data pipelines for high-power laser systems. Collaborate with R&D, production and service teams to build condition monitoring and predictive maintenance solutions, transform analytics into scalable cloud applications handling large machine and signal data, and maintain cloud data infrastructure with architects, IT teams, customers and suppliers.

Lead development and scaling of data processing pipelines for road and navigation products. Design and implement new pipelines, monitor performance metrics, and enforce security best practices. Collaborate within a cross-functional team to build high-quality, distributed data systems supporting mapping and navigation features.

Design, build, and operate governed data pipelines and context layers for AI agents integrating Salesforce, SAP, and service data. Implement feedback loops, data-quality frameworks, semantic models, Power BI reporting, and secure API-based integrations to support enterprise commercial AI workflows.

Build auditable cloud data pipelines and perform statistical analyses on clinical and medical imaging datasets. Apply classical ML, deep learning, and LLM methods to extract and analyze clinical data, maintain data lineage and reproducibility, create dashboards and observability tooling, and produce evidence suitable for regulatory submissions and audits.

Lead design and implementation of a scalable modern data platform and real-time pipelines. Build, optimize, and maintain data models, ETL/ELT pipelines and integrations (Kafka, Spark, Delta Live Tables), enforce data governance and quality, enable AI/ML workflows and MLOps, mentor the team, evaluate new technologies, and collaborate with product and analytics stakeholders to deliver reliable, self-service data products.

Build, optimize, and maintain data pipelines and data systems using Python and PySpark. Evaluate and annotate AI-generated ETL outputs, ensure data accuracy and scalability, integrate data solutions with software teams, document processes, follow data governance, and support backlog and best practices under senior guidance.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain enterprise-scale data platforms and ETL/ELT pipelines. Develop software to ingest, parse, and process structured and unstructured data. Guide cross-functional teams, operationalize data workflows, and support scaling, monitoring, and operation of data solutions for mission-driven clients.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.