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

Carl Zeiss SMT GmbH; Oberkochen; Vor Ort; EUR 75.000 - 110.000; Vollzeit

Zusammenfassung

Carl Zeiss SMT GmbH is seeking a Senior Data Engineer to design and implement data models that link development, manufacturing, SAP, and supply‑chain data in a high‑tech environment. You will build production data pipelines with Kafka, dbt, Trino, and Databricks, ensure data quality and governance, and translate complex physics‑based processes into robust analytics.

Take ownership, mentor junior engineers, and contribute to architecture decisions while operating in a German‑English bilingual

Qualifikationen

• 5–7 years data modeling experience (Data Vault, dimensional, logical/physical) in a complex manufacturing/high‑tech setting.
• Bridge physics and data by collaborating with domain experts to translate complex processes into robust data models.
• Experience profiling, cleaning, and taming heterogeneous, historically grown data sources.
• Hands-on with Trino (on‑prem), dbt, Apache Kafka, and Databricks; knows strengths/limits of each tool.
• Familiarity with GenAI models and safe-use to improve daily work; understand limitations.
• SAP/MM/PP/SD/QM and MES/SCADA/PLM data; integrating sources into analytics platform.
• Emphasis on data governance: quality rules, lineage, metadata from the outset.
• Excellent communication across levels in German and English; senior, independent thinking.

Aufgaben

• Conceptualize, implement, and develop data models linking development, manufacturing, SAP, and supply‑chain data.
• Translate physical and process requirements into OLAP/OLTP, Data Vault, and dimensional models.
• Collaborate with process/domain experts to clarify definitions, thresholds, quality rules, and compliance.
• Design and implement data governance, quality checks, metadata management, and lineage tracking.
• Implement production data pipelines (ETL/ELT) via Kafka Streams, dbt, on‑prem Trino and cloud Databricks with CI/CD.
• Ensure data consistency, visibility, and availability for analytics, AI/ML models, and simulations.
• Develop performance and scaling strategies including monitoring, profiling, and tuning.
• Mentor less experienced Data Engineers, promote best practices and code reviews.
• Contribute to architecture decisions, security‑by‑design, and data privacy requirements.

Kenntnisse

Data modeling

Data governance

Cross-domain collaboration

Mentoring

German & English communication

Senior mindset

Tools

Trino

dbt

Apache Kafka

Databricks

Jobbeschreibung

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

Your ZEISS Recruiting Team:

Adrian Kahl

Step out of your comfort zone, excel and redefine the limits of what is possible. That’s just what our employees are doing every single day – in order to set the pace through our innovations and enable outstanding achievements. After all, behind every successful company are many great fascinating people. In a spacious modern setting full of opportunities for further development, ZEISS employees work in a place where expert knowledge and team spirit reign supreme. All of this is supported by a special ownership structure and the long-term goal of the Carl Zeiss Foundation: to bring science and society into the future together. Join us today. Inspire people tomorrow.

Diversity is a part of ZEISS. We look forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, philosophy of life, disability, age, sexual orientation or identity.