Top Data Engineer Jobs in Germany

Responsible for deploying and maintaining data center infrastructure, troubleshooting hardware and network issues, managing inventory, and setting up data centers.

Design, build and improve scalable cloud-native data pipelines and architectures using Azure and Databricks. Collaborate with teams to process large datasets, improve performance, ensure data quality, and apply CI/CD practices for deployments.

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.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

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.

Build and maintain scalable, reliable data infrastructure and self-service features for ETL, orchestration, and real-time analytics. Optimize compute and costs, automate operations for high availability, and contribute to designing and implementing new platform services.

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.

Build and maintain automated, near real-time data pipelines and BI solutions to optimise cloud, SaaS, and AI costs. Deliver analytics, dashboards, anomaly detection and forecasting using AI/ML, collaborate with users for excellent data experience, and contribute to data and software architecture in a multi-cloud FinOps environment.

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.

Build and maintain scalable AWS-based data pipelines and a lakehouse, prepare and model structured and unstructured data for reporting, analytics and AI, implement testing/monitoring, collaborate with product and engineering teams, and promote best practices across the data organization.

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 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.

Build and maintain pipelines that process petabytes of raw and semi-raw data, transform large-scale datasets about domains and brands, implement analytics for marketers and SEO analysts, and deploy/manage production systems using Go, ClickHouse, and CI/CD practices.

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.

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.

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.

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.

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.

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, 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.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.