Level: Senior

Job Feld: BI, Data

Anstellung: Vollzeit

Vertragsart: Unbefristetes Dienstverhältnis

Ort: Oberkochen

Arbeitsmodell: Onsite

Job Zusammenfassung

In dieser Position entwickelst du robuste Datenmodelle und implementierst ETL-Pipelines, während du eng mit Domain-Experten zusammenarbeitest, um Datenqualität und Governance zu gewährleisten.

Job Technologien

Deine Rolle im Team

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.

Unsere Erwartungen an dich

Qualifikationen

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.
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.

Erfahrung

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: 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.