Data Analytics Engineer / Architect (m/f/d)

As a Data Analytics Engineer / Architect, you will design and optimise scalable data pipelines and models to enable reliable insights for the automotive decision-makers. You will shape the semantic layer and data architecture to support BI and advanced analytics across the Automotive unit. Collaborating with business stakeholders, you translate requirements into trusted data assets and ensure a single source of truth. The role emphasizes modern platforms like Snowflake and Databricks, and a strong focus on SQL performance and dbt-driven workflows. This is a hands-on opportunity to impact how data informs strategy in a fast-moving automotive information business.

• 30 fixed vacation days per year
• flexible working hours
• job bike leasing
• e charging stations
• public transport tickets
• company doctor

• Design, build, and optimise scalable data transformation pipelines with emphasis on SQL performance and efficient dbt models
• Develop and evolve data models using dimensional modeling, Data Vault 2.0, and Anchor Modeling approaches
• Define, standardise, and maintain business entities, KPIs, and metric logic for reusable semantic assets
• Provide semantic layers consumable by BI tools and ML use cases
• Collaborate with business stakeholders to translate requirements into scalable, consistent data models
• Contribute to Git-based development workflows with code reviews and best practices
• Leverage Snowflake and/or Databricks and support data quality and governance within the data platform
• Explore or implement data testing, cataloging, and lineage tracking to ensure data trust

• Hands-on Snowflake expertise and data modeling for business entities, KPIs, and semantic layers
• Strong SQL transformations and performance focus
• Hands-on dbt experience (models, tests, macros, project structure)
• Knowledge of dimensional modeling, Data Vault 2.0, and Anchor Modeling (or willingness to learn)
• Ability to translate requirements into scalable data models and maintain data quality
• Experience with Git-based development workflows
• Nice-to-have: Power BI consumption knowledge, data testing frameworks, data cataloging/lineage, feature engineering for ML

• collaborative
• stakeholder communication
• strong problem-solving
• Snowflake data modeling
• Databricks
• SQL