(Senior) Data Modeler / Data Vault Expert (Azure Databricks / Lakehouse) | (Senior) Data Modeler / Data Vault Expert Azure Databricks / Lakehouse (m/w/d)

Deichmann SE

Essen, Ruhr, Nordrhein-Westfalen, Deutschland
Published Feb 10, 2026
Full-time
Permanent

Job Summary

As a (Senior) Data Modeler and Data Vault Expert, you will serve as the architect for a modern Azure-based Lakehouse platform. Your day-to-day responsibilities involve designing and evolving the Enterprise Data Model with a heavy focus on Data Vault 2.0 methodologies, including the modeling of Hubs, Links, and Satellites. You will define modeling standards and naming conventions while collaborating closely with Data Engineers and BI teams to translate business requirements into robust data structures. This role is unique because it combines high-level architectural strategy with hands-on technical implementation in a modern cloud environment. You will ensure data quality and traceability across the entire lifecycle, supporting the integration of new source systems and contributing to automation and orchestration decisions. This position offers significant creative freedom and the opportunity to shape a scalable information architecture within a collaborative, professional team based in Essen.

Required Skills

Education

Completed degree in (Business) Informatics, Mathematics, Natural Sciences, or a comparable qualification.

Experience

  • Several years of professional experience in data modeling with a focus on Data Vault 2.0
  • Proven experience in Cloud or Lakehouse environments, specifically Microsoft Azure
  • Practical experience with Azure Databricks, Spark, and programming in Python or Scala
  • Professional experience in designing integration models, including Business Keys and Surrogate Keys
  • Experience working with ETL/ELT processes and metadata documentation

Languages

German (Fluent)English (Basic)

Additional

  • Location: Essen, Germany. Flexible working hours and mobile work options available. Candidates must provide salary expectations and earliest possible start date.