Senior Data Engineer

Your role in the team

Our expectations of you

Qualifications

• Brücke zwischen Physik und Daten: Nachweisliche Fähigkeit, mit Fachexperten in Fertigung, Entwicklung oder Ingenieurwesen zusammenzuarbeiten und hochkomplexe, physikalisch fundierte Prozesse in robuste Datenmodelle zu übersetzen.
• 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.
• 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.

Experience

• 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.
• Turning poor data quality into a 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.
• 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.
• Senior mindset: Make independent architectural decisions, mentor less experienced colleagues, and demonstrate a pragmatic, solution-oriented approach even in the face of uncertain or poor data conditions.