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Senior Data Engineer – f/m/d
ZEISS Group. Conceptualize, implement, and further develop data models linking development, manufacturing, SAP, and supply-chain data .
Tech Stack
Tools & technologiesApacheKafkaSparkVault
About the role
Key responsibilities & impact- Conceptualize, implement, and further develop data models linking development, manufacturing, SAP, and supply-chain data
- Translate physical and process requirements into robust, traceable data models using OLAP/OLTP, Data Vault, and dimensional modeling
- Collaborate with process and domain experts to clarify definitions, thresholds, quality rules, and compliance requirements
- Design and implement data governance, quality checks, metadata management, and lineage tracking
- Implement production data pipelines using Kafka Streams, dbt transformations, Trino, Databricks, and CI/CD with quality gates and automated tests
- Ensure data consistency, visibility, and availability for analytics, AI/ML models, and simulations
- Develop performance and scaling strategies, including monitoring, profiling, and performance tuning
- Mentor less experienced Data Engineers, promote best practices, and conduct code reviews
- Contribute to architecture decisions, security-by-design, and data privacy requirements
Requirements
What you’ll need- 5–7 years of cross-domain data modeling experience
- Strong expertise in Data Vault, dimensional, logical, and physical data modeling
- Experience collaborating with manufacturing, development, or engineering domain experts
- Experience profiling, assessing, and cleaning heterogeneous, historically grown data sources
- Hands-on experience with Trino, dbt, Apache Kafka, and Databricks, including Delta Lake and Spark
- Familiarity with state-of-the-art GenAI models, their limitations, and safe-use requirements
- Familiarity with SAP data structures (MM, PP, SD, QM)
- Experience with MES/SCADA or PLM data and integrating these sources into analytical data platforms
- Knowledge of data governance, quality rules, lineage, and metadata management
- Ability to communicate complex data architectures with process engineers, management, and data scientists
- German and English language proficiency
- Ability to make independent architectural decisions and mentor less experienced colleagues
- Pragmatic, solution-oriented approach in uncertain or poor data conditions