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

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, they have become an integral part of our everyday lives – whether in smartphones, smart homes or smart factories. ZEISS is a technology leader in the field of semiconductor manufacturing equipment. With high\-precision lithography optics, photomask systems and process control solutions, ZEISS enables 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.

Your role:

• 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


• Development of performance and scaling strategies 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

Your profile

• 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


• Bridge between physics and data: Proven ability to collaborate 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; also 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 sources 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 independent architectural decisions, mentor less experienced colleagues, and demonstrate a pragmatic, solution\-oriented approach even in the face of uncertain or poor data conditions

Adrian Kahl

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