(Senior) Data Engineer

ZEISS Semiconductor Manufacturing Technology​

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

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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

Your ZEISS Recruiting Team:

Adrian Kahl