Responsibilities
- Design, build, and operate data pipelines and platforms.
- Develop, test, and operate robust ETL/ELT pipelines.
- Design and maintain data models, schemas, and structures for data warehouses/lakehouses.
- Implement validations, quality checks, and monitoring.
- Build and optimize data solutions in the cloud (AWS/Azure/GCP).
- Automate recurring data workflows (e.g., using Airflow) and ensure clean deployments with CI/CD.
- Collaborate closely with cross-functional teams and clients.
Requirements
- Data engineering experience: built and operated production data pipelines and infrastructure.
- Tool stack: proficient in Python and SQL, familiar with ETL/ELT, data warehouses/lakehouses, and cloud environments (Databricks/Snowflake/AWS/Azure/GCP).
- Data modeling & quality awareness: understand data modeling and prioritize data quality, testing, and maintainable code.
- Execution skills & ownership: pragmatically and purposefully drive solutions from concept to production and take responsibility for results and quality.
- Team player: enjoy working with data scientists, analysts, and clients.
- Languages: fluent German (C1+); strong English skills are a plus.
Core Competencies
Demonstrates expertise in designing and operating data pipelines and platforms, with a strong focus on ETL/ELT processes, data modeling, and quality assurance. Proficient in cloud environments and automation tools, ensuring efficient data workflows and collaboration with cross-functional teams.
