Job
In this role, you develop robust data models and implement ETL pipelines while closely collaborating with domain experts to ensure data quality and governance.
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Job technologies
Your role in the team
- Conceptualization, implementation, and further development of data models that seamlessly link development, manufacturing, SAP, and supply-chain data.
- Translating physical and process requirements into robust, traceable data models (OLAP/OLTP, Data Vault, dimensional modeling).
- Collaboration with process and domain experts to clarify definitions, thresholds, quality rules, and compliance requirements.
- Design and implementation of data governance, quality checks, metadata management, and lineage tracking.
- Implementation of production data pipelines (ETL/ELT) via Kafka Streams, dbt transformations, and on-prem (notably Trino) as well as cloud environments (notably Databricks) using CI/CD (Quality Gates, automated tests).
- Ensuring data consistency, visibility, and availability for analytics, AI/ML models, and simulations.
- Entwicklung von Performance- und Skalierungsstrategien, einschließlich Monitoring, Profiling und Performance-Optimierung.
- Mentoring less experienced Data Engineers, promoting best practices and code reviews.
- Contributions to architecture decisions, security-by-design, and data privacy requirements.
Our expectations of you
Qualifications
- Brücke zwischen Physik und Daten: Nachweisliche Fähigkeit, mit Fachexperten in Fertigung, Entwicklung oder Ingenieurwesen zusammenzuarbeiten und hochkomplexe, physikalisch fundierte Prozesse in robuste Datenmodelle zu übersetzen.
- Seizing new technologies: Very good familiarity with state-of-the-art GenAI models and their reliable use to improve and accelerate daily work; also aware of their limits and safe-use requirements.
- Data governance as a discipline: Embedding quality rules, lineage, and metadata from the outset in pipelines and models - governance is not overhead but part of good engineering.
- Communication strength at all levels: Ability to discuss complex data architectures clearly and purposefully with process engineers, management, and data scientists - in German and English.
Experience
- Strong data modeling expertise: 5-7 years of cross-domain data modeling experience (Data Vault, dimensional, logical/physical) - ideally in a complex manufacturing or high-tech environment.
- Turning poor data quality into a strength: Experience in systematic profiling, assessment, and cleaning of heterogeneous, historically grown data sources - you see data chaos as a design challenge, not a hurdle.
- Mastery of a hybrid tech stack: Hands-on experience with Trino (on-prem), dbt (transformation & documentation), Apache Kafka (streaming), and Databricks (Delta Lake, Spark); know the strengths and limits of each tool.
- SAP and supply chain data competence: Familiarity with SAP data structures (MM, PP, SD, QM) as well as MES/SCADA or PLM data; experience integrating these sources into an analytical data platform.
- Senior mindset: Make independent architectural decisions, mentor less experienced colleagues, and demonstrate a pragmatic, solution-oriented approach even in the face of uncertain or poor data conditions.
Benefits
Work-Life-Integration
Health, fitness & fun
Topics you will work on
Job locations
About your employer
ZEISS is a world-leading technology company that is active in the fields of optics and optoelectronics. In the last financial year, ZEISS generated sales revenues of more than 6.4 billion euros through its four segments Semiconductor Manufacturing Technology, Industrial Quality & Research, Medical Technology and Consumer Markets (as at 30 September 2019).
- 50-249 Employees
- Established Company
- Onsite, Hybrid, Full Remote
- Industry, Production
Employer reviews
- Team culture
- 4.0
- Working conditions
- 4.6
- Career growth
- 3.6
- Engineering
- 2.7
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