Data Engineer (all genders)
Köln
Full-time
Permanent employee
Job overview
Our Data Engineering team plays a critical role in this mission, transforming raw data into high-quality, reliable assets for reporting, analytics, and advanced data products. You’ll be part of a collaborative team of Data Engineers, and supported by a dedicated Data Architect and Program Manager for alignment across tech and business.
What you will do
- Design, build, and maintain reliable and scalable data pipelines to support analytics, reporting, and future ML use cases
- Contribute to our modern cloud-based data platform in Azure and Databricks
- Collaborate closely with Data Analysts and Data Scientists to co-create data products
- Support and contribute to our internal data platform alongside our Data Platform Engineer
- Continuously improve data quality, documentation, and performance
- Champion best practices in DataOps: CI/CD, monitoring, alerting, testing, and infrastructure-as-code
Job Title
Data Engineer
What you will need
- 7+ years of experience in data engineering, ideally with some hands-on exposure to analytics engineering practices (e.g., data modeling, transformation logic)
- Deep understanding of data pipeline orchestration, distributed processing, and building resilient, testable ETL/ELT systems
- Expertise in Scala or Python with strong SQL skills and experience using Spark in production environments
- Experience with cloud-native architectures, especially Azure Cloud Platform and Databricks
- Familiarity with streaming data frameworks (Kafka, Event Hubs, or similar)
- Solid grasp of data modeling concepts, especially in the context of analytics and reporting (conceptual/logical/physical models)
- Languages & Frameworks: Scala, Python, SQL, Spark
- Cloud & Infra: Azure Cloud Platform, Databricks, Kafka, Azure DevOps/GitHub, Terraform
What you will bring
- Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders
- Strong collaboration mindset to work effectively with cross-functional teams including data science, engineering, and business units
- High attention to detail with a strong focus on data quality, accuracy, and reliability
- Skilled in stakeholder management, capable of balancing business needs with technical feasibility
- Self-starter with strong organizational skills and the ability to drive initiatives from concept to completion
Inclusivity
All genders
Workplace type
Hybrid
