(Senior) Data Engineer (f/m/d)

Trust something new, grow beyond itself and redefine the boundaries of the feasible. That is exactly what our employees can and should live daily. To set the pace with our innovations and make it great. Because there are a lot of fascinating people behind every successful company.

ZEISS employees work in an open and modern environment with numerous development and further training opportunities. Our culture is characterized by expert knowledge and team spirit. All this is supported by the special ownership structure and the long-term goal of the Carl-Zeiss Foundation: to advance science and society together.

Today dare. Inspire tomorrow.

Diversity is part of ZEISS. We look forward to your application regardless of gender, nationality, ethnic and social origin, religion, world view, disability, age as well as sexual orientation and identity.

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ZEISS Semiconductor Manufacturing Technology

Enabler for smaller, more powerful, and more energy-efficient microchips

Working for tomorrow today. Around 80 percent of all microchips worldwide are produced using ZEISS technologies. As the centerpiece of every electronically controlled system, You have become an integral part of our everyday lives – whether in smartphones, smart homes or smart professionals. ZEISS is a technology leader in the field of semiconductor manufacturing equipment. With high-precision lithography optics, photomask systems and process control solutions, ZEISS driving the production of ever smaller, increasingly powerful, and more energy-efficient microchips, and thus plays a pivotal role in the age of micro- and nanoelectronics.

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 definitions, thresholds, quality rules, and compliance 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 Development of performance and scaling including monitoring, profiling, and performance tuning Mentoring less experienced Data Engineers, promoting best practices and code reviews Contributions to architecture decisions, security-by-design, and data privacy requirements

Strong data modeling expertise: 5–7 years of cross-domain data modeling experience (Data Vault, dimensional, logical/) — ideally in a complex manufacturing or high-tech environment Bridge between physics and data: Proven ability to cooperate with domain experts in manufacturing, development, or engineering and translate highly complex, physically grounded processes into robust data models Turning poor data quality into an 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 Seizing new technologies: Very good familiarity with state-of-the-art GenAI models and their reliable use to improve and accelerate daily work; so aware of their limits and safe-use requirements 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 into an analytical data platform 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 Senior mindset: Take architectural independent decisions, mentor less experienced colleagues, and a demonstrates pragmatic, solution-oriented approach even in the face of uncertain or poor data conditions