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

Description

The company makes high-precision lithography optics, photomask systems and process control solutions for semiconductor manufacturing equipment.

Around 80 percent of the world's microchips are made with its technologies, which help produce smaller, more powerful and more energy-efficient chips used in phones, smart homes and smart factories.

Responsibilities

• Build data models that connect development, manufacturing, SAP and supply-chain data.
• Turn physical and process requirements into sturdy, traceable models using OLAP/OLTP, Data Vault and dimensional modeling.
• Work with process and domain experts to pin down definitions, thresholds, quality rules and compliance needs.
• Set up governance rules, quality checks, metadata handling and lineage tracking.
• Build production ETL/ELT pipelines with Kafka Streams and dbt, on-prem on Trino and in the cloud on Databricks, using CI/CD with quality gates and automated tests.
• Keep data consistent, visible and available for analytics, AI/ML models and simulations.
• Plan performance and scaling work, including monitoring, profiling and tuning.
• Coach less experienced data engineers, share good practice and review their code.
• Help shape architecture decisions, security by design and data privacy measures.

Requirements

• 5, 7 years of cross-domain data modeling experience (Data Vault, dimensional, logical and physical), ideally in complex manufacturing or high-tech settings.
• Ability to work with domain experts in manufacturing, development or engineering and turn complex, physics-based processes into solid data models.
• Experience profiling, assessing and cleaning heterogeneous, historically grown data sources. You treat messy data as a design problem.
• Hands-on work with Trino (on-prem), dbt, Apache Kafka and Databricks (Delta Lake, Spark), and a clear sense of each tool's strengths and limits.
• Very good familiarity with current GenAI models, using them reliably to speed up daily work, and knowing their limits and safe-use rules.
• Familiarity with SAP data structures (MM, PP, SD, QM) and MES/SCADA or PLM data, plus experience bringing these into an analytical data platform.
• Building quality rules, lineage and metadata into pipelines and models from the start, treating governance as part of good engineering.
• Communicating complex data architectures clearly with process engineers, managers and data scientists, in German and English.
• Senior mindset: make independent architecture decisions, mentor colleagues and stay pragmatic when data is uncertain or poor.