Top Data Engineer Jobs in Germany

As a Senior Engineer, you'll enhance optimization systems, build data layers, design math models, and influence architectural standards in backend and data-intensive solutions.

Own and evolve the end-to-end customer identity resolution pipeline, improve probabilistic matching models, build scalable BigQuery/GCS/Cloud Run data pipelines, add monitoring and validation, and collaborate with stakeholders to deliver reliable, production-grade identity master data.

Lead ownership of data ingestion and warehouse pipelines, manage and improve the data platform (Kafka -> BigQuery), implement DWH best practices, collaborate with DevOps and R&D, and support data modelling to enable company-wide analytics.

Design and build scalable data lakehouse solutions and ETL/ELT pipelines (Spark/pySpark/Glue/Kinesis) to support ML and reporting. Implement data models, integrate sources into Snowflake/warehouses, ensure compliance and security, monitor pipelines with CI/CD and Airflow, and mentor junior engineers.

Design, develop, and deploy production-grade ML and AI solutions including LLM fine-tuning, RAG architectures, NLP pipelines, time-series forecasting, supplier risk and anomaly detection, scalable data pipelines, and RESTful integrations with enterprise systems.

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.

The Data Engineer will design and maintain data pipelines, optimize ETL processes, ensure data quality, and collaborate in an agile environment.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.