Top Data Engineer Jobs in Germany

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.

Design, implement, and optimize multi-cloud (primarily GCP, also AWS/Azure) data environments. Build data pipelines, migrate databases, manage cloud infrastructure (Terraform/Ansible), deploy containerized workloads, and produce BI reports and data inventories. Support DevSecOps CI/CD, scripting automation, and provide cost/optimization recommendations for mission-critical programs requiring active Secret clearance.

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.

The Data Engineer will develop scalable data processing solutions, facilitate data ingestion, and ensure data quality while mentoring junior engineers.

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.

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, 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.

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.

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.

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.

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 and maintain scalable ETL/data pipelines and orchestration for content publishing, optimize and monitor data infrastructure, automate processes, and collaborate with data and content teams.

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 data pipelines and features to improve supply-chain vendor experience. Ensure pipeline reliability and scalable cloud architectures, implement CI/CD for ML models, evolve system architecture and engineering practices, and collaborate with data scientists to optimize model lifecycle, monitoring, and alerting.

The Finance Data Engineer will design and deploy automated pipelines, ensure finance data integrity, and implement AI-driven solutions while maintaining data sovereignty and compliance with government contracts.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

Build scalable, searchable data infrastructure and ML pipelines for autonomous driving systems while collaborating with engineering teams to ensure data quality and integrity.

Design and develop data models, ETL processes, and cloud-based data solutions using Azure and various big data technologies. Support report generation and data analysis.

The Senior Data Platform Engineer at Taxfix will design and maintain data infrastructure, build pipelines for analytics and compliance, ensure data quality, and prepare data for AI projects.

As a Senior Data Architect, you'll design and evolve data architecture, ensuring scalability and reliability, define data modeling standards, build data pipelines, enhance data quality, and collaborate with teams to optimize data as a strategic asset.

Design, build, and operationalize scalable AI/ML applications and data pipelines. Retrieve, parse, and process structured and unstructured data to support model training and deployment. Code in Python or TypeScript, collaborate with cross-functional teams, and maintain enterprise-scale ML systems. Support monitoring, scaling, and operation of AI/ML platforms while working on mission-driven projects requiring a Top Secret clearance.