Job
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.
Benefits
Work-Life-Integration
Gesundheit, Fitness & Fun
Themen, mit denen du dich im Job beschäftigst
Job-Standorte
Das ist dein Arbeitgeber
ZEISS ist ein führendes internationales Technologieunternehmen, das in den Bereichen Optik und Optoelektronik tätig ist.
- 50-249 Mitarbeitende
- Etablierte Firma
- Onsite, Hybrid, Full Remote
- Industrie, Produktion
Arbeitgeber-Reviews
- Teamkultur
- 4,0
- Arbeitsbedingungen
- 4,6
- Karriere
- 3,6
- Engineering
- 2,7
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