Top Data Engineer Jobs in Germany

Implement and operationalize data pipelines and workflows to support production AI. Design, build, and maintain enterprise-scale analytic solutions; guide cross-functional teams of analysts, engineers, and developers; perform analytical exploration and data examination; deliver reporting and visualization solutions. Position requires travel up to 25% and TS/SCI clearance.

Designs, builds, and improves data workflows, backend services, AI agents, pipelines, and workflow orchestration systems. The role centralizes fragmented tools, supports production-grade internal platforms, improves reliability and automation, and translates analyst and stakeholder requirements into scalable technical solutions. Requires strong Python, SQL, data modeling, AI application, Git, and data engineering skills, with collaboration across analysts, engineers, and business stakeholders.

Design and deliver scalable data pipelines for model development and production ML workflows in autonomous driving. Ingest, transform, curate, validate, and monitor large-scale real-world, synthetic, and partner datasets. Improve pipeline performance and collaborate with ML engineers, Data Corpus, AI Platform, and partners to ensure datasets are reliable, traceable, and model-ready.

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.

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.

Gather, analyze, and interpret supply-related data; translate findings into actionable business insights; automate reporting; design and maintain Looker Studio dashboards; support ad hoc analysis; ensure data quality; and collaborate with engineers, data scientists, and business stakeholders to promote data-driven decision-making.

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.

Build and own scalable Azure data platforms, SQL Server and Databricks environments, end-to-end pipelines, and maintainable data models. Apply Kimball dimensional modeling, solve complex client data problems, challenge unclear requirements, and communicate technical concepts clearly to technical and non-technical stakeholders. The role emphasizes production reliability, client impact, continuous learning, and collaboration in a fully remote European data consultancy.

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.

Build high-fidelity simulation environments and synthetic camera and LiDAR data for autonomous off-road vehicles. Develop scenarios, data augmentation, domain randomization, and Sim2Real pipelines to improve perception and autonomy systems. Integrate simulation modules with ROS2 robotics software and collaborate with AI and robotics teams to accelerate model training, testing, and validation.

The Software Engineer will focus on data collection and integration for model training at Speechify, improving data quality and pipeline infrastructure.

This role involves data collection for model training, operating cloud infrastructure on GCP, and collaborating on data strategies for AI products.

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.

Operate and maintain data ingestion, processing, analysis, and product-distribution systems for space missions. Responsibilities include performance monitoring, operational readiness, testing, documentation, training, anomaly investigation, first-line maintenance, on-call support, external coordination, and formal reviews. The role also supports integration, verification, validation, configuration control, procedure development, and quality control for remote-sensing and Earth-observation data-processing systems.

Designs and maintains high-throughput Reverse ETL pipelines connecting core platforms with reporting tools. Evaluates storage engines and data models across MySQL, OpenSearch, BigQuery, and ClickHouse; modernizes legacy processing architecture; and partners with engineering teams on scalable information architecture. The role also drives monitoring, reliability, maintainability, technical standards, and secure migration strategies for data infrastructure.

Own and scale the data platform supporting multi-tenant industrial optimization applications. Responsibilities include ingesting heterogeneous customer data, designing storage and query paths for millions of records, managing PostgreSQL performance and migrations, building infrastructure and deployment systems, improving optimization-engine performance, and shipping and operating features in production. The role works across Python, TypeScript, cloud infrastructure, APIs, data pipelines, and the broader product stack.

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 the end-to-end analytics foundation for a new mobile game, including gameplay tracking, data architecture, pipelines, semantic models, reporting, and AI-powered insight workflows. Use agentic engineering tools to accelerate development while owning correctness, quality gates, and production review. Analyze player behavior, monetization, attribution, anomalies, and trends to support product and leadership decisions. Establish organizational standards for AI-assisted development and independently drive technical initiatives from concept through production.

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.