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

ZEISS Group; Oberkochen; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

ZEISS Semiconductor Manufacturing Technology is seeking a senior Data Engineer to conceptualize, implement, and advance data models linking development, manufacturing, SAP, and supply-chain data.

You will design governance, metadata, and lineage, and build end-to-end data pipelines with Kafka, dbt, and Trino in on-prem and Databricks cloud environments. Mentoring peers and shaping architecture are key aspects of the role.

Qualifikationen

• Strong data modeling expertise across Data Vault and dimensional modeling.
• Experience translating physics/engineering processes into data models.
• Experience profiling, cleaning, and governing heterogeneous data sources.

Aufgaben

• Design and implement data models linking development, manufacturing, SAP, and supply-chain data.
• Develop data governance, metadata management, and lineage tracking.
• Build production data pipelines (ETL/ELT) using Kafka, dbt, and Trino.
• Mentor junior data engineers and contribute to architecture decisions.

Kenntnisse

Data modeling

Data Vault

Dimensional modeling

SAP data structures

Kafka

dbt

Trino

Databricks

Tools

Trino (on-prem)

dbt

Apache Kafka

Databricks (Delta Lake, Spark)

Jobbeschreibung

ZEISS Semiconductor Manufacturing TechnologyEnabler for smaller, more powerful, and more energy-efficient microchipsWorking 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 dataTranslating 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 requirementsDesign and implementation of data governance, quality checks, metadata management, and lineage trackingImplementation 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 simulationsDevelopment of performance and scaling strategies including monitoring, profiling, and performance tuningMentoring less experienced Data Engineers, promoting best practices and code reviewsContributions to architecture decisions, security-by-design, and data privacy requirementsYour 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 environmentBridge 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 modelsTurning 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 hurdleMastery 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 toolSeizing 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 requirementsSAP 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 platformData 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 engineeringCommunication strength at all levels: Ability to discuss complex data architectures clearly and purposefully with process engineers, management, and data scientists — in German and EnglishSenior 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 conditionsYour ZEISS Recruiting Team:Adrian Kahl