Top Data Engineer Jobs in Germany

As a Data Platform Engineer, you will architect and build high-performance data platforms focusing on distributed computing, ETL processes, and metadata modeling, collaborating closely with the CTO and product team.

Develop and maintain healthcare data management systems, focusing on data APIs, microservices, and infrastructure while adhering to healthcare standards.

Design, build, and maintain backend services and automation for data pipelines and internal tooling. Integrate AI/ML features, own CI/CD and observability, lead code reviews, and collaborate with cross-functional teams to ensure scalable, reliable production systems.

The Software Engineer will focus on maintenance and enhancement of Earth Observation Data Processing systems using C++. Responsibilities include SW integration, validation, testing, packaging, installation, and coding to refine existing systems based on mission needs.

Develop and maintain scalable data streaming products, contribute to backend services and SDKs, mentor team members, and improve tech-debt in an agile environment.

As a Senior Data Engineer, you will architect and scale data platforms with Databricks, manage data pipelines, ensure data quality, and collaborate with teams on data-driven solutions.

The role involves developing AI platforms, mentoring engineers, optimizing solutions, and contributing to the overall product strategy for Snowflake's AI tools.

The role involves market analysis, concept development for signal transfer systems, customer requirement validation, document creation, and technology development in data and signal transfer.

As a Senior Staff Data Engineer, you will lead data engineering practices, establish best practices, mentor teams, and design secure data solutions. Collaboration with various teams is essential to align on goals and ensure compliance with data regulations.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

As a Senior Data Engineer, you'll build scalable solutions for the Data Platform, develop ETL processes, ensure data quality, conduct data modeling, and guide junior engineers.

Design and develop a platform to enhance simulation and stellarator design processes, manage cloud infrastructure, and improve DevOps practices.

As a Software Engineer, you will develop and optimize real-time data pipelines for athlete tracking, ensuring high-performance software solutions for sports analytics.

Design and implement search retrieval and ranking systems, build data pipelines, and evaluate performance metrics. Collaborate with teams to enhance system relevance.

As a Data Platform Engineer, you will design, build, and maintain a cloud data platform, ensuring its reliability and efficiency while supporting AI integration and collaboration with internal teams.

Design and validate cloud data, analytics, and GenAI/RAG architectures; lead customer discovery, craft solution designs and PoCs, deliver demos, and communicate technical value to stakeholders.

The Data Ops Engineer will design and build data pipelines, develop automated systems for insights delivery, enable AI workflows, and optimize data strategy.

The Senior Data Engineer will design scalable systems, automate ML operations, optimize performance, and enhance infrastructure for data processing and monitoring.

Design and maintain data pipelines, collaborate with stakeholders, ensure data quality, deploy infrastructure as code, and optimize reporting solutions.

The role involves maintaining EUMETSAT Copernicus software, correcting anomalies, optimizing performance, and documenting the code while working collaboratively in a team.

The DevOps Engineer will maintain application environments, ensure infrastructure availability, oversee deployment processes, and lead training sessions within the team.

Build and operate Python/PySpark production data pipelines (including near real-time), ensure data quality, create core data models and reporting pipelines for AML/Risk, support regulatory reporting and data governance, and collaborate with cross-functional teams to deploy scalable, auditable data solutions.

Design and build reliable, performant data pipelines for large payment datasets. Own end-to-end data flows, drive data quality, implement transformations (dbt), operate on StarRocks and Flink/Spark in cloud (AWS/GCP), collaborate cross-functionally, establish engineering standards, mentor engineers, and shape CI/CD and incident practices.

Design, build, and maintain scalable data transformation pipelines on Snowflake using DBT. Integrate diverse data sources (databases, files, HDFS, APIs), optimize DBT models, automate workflows, and support BI/reporting through monitoring and troubleshooting to ensure reliable data delivery.

The Technical Privacy Expert will implement privacy-by-design principles, ensure compliance with regulations, mentor teams, and develop technical frameworks for privacy in technologies.