Analytical Engineer (f/m/d)

Awin is part of the Axel Springer group, operating in the data and analytics domain. The company focuses on leveraging data assets to drive strategic insight, optimize campaign performance, and empower client-facing teams with intelligence. The role is within the BI team, working on a modern Databricks stack with Tableau and Power BI as primary BI tools.

• Strong hands-on SQL skills, including complex transformations and query optimization.
• Proven experience with dbt (Core or Cloud) in production.
• Experience with cloud data warehouses or lakehouses, preferably Databricks.
• Solid understanding of data modeling concepts such as dimensional modeling and star schemas.
• Comfortable with code-reviewed, version-controlled workflows.
• Experience working with BI teams, understanding BI Developers and Insight Analysts needs.
• Ability to translate ambiguous business requirements into clear data definitions.
• Strong communication skills to explain technical concepts to non-technical stakeholders.

Highly desirable:

• Experience with Tableau and/or Power BI and understanding of BI data consumption.
• Familiarity with Databricks, including Delta Lake and notebook development.
• Experience with semantic layer or governance tools like Unity Catalog, dbt Semantic Layer, or Cube.
• Experience mentoring or upskilling colleagues in data tooling.
• Exposure to data observability or data quality tools.
• Exposure to AI or GenAI data requirements and AI-ready data concepts.

Build the Analytics Engineering practice:

• Provide business context for data mart layers supporting self-serve analytics.
• Create a single source of truth for business metric definitions accessible to all BI tools.
• Own data quality initiatives and documentation around the semantic layer.

Work across teams:

• Partner with Data Engineering on handoff boundaries and data quality.
• Collaborate with Insight Analysts to encode business logic into reusable data models.
• Support BI Developers in using the semantic layer within Tableau and Power BI.
• Participate in cross-departmental syncs with Data Engineering.

Upskill and develop the team:

• Coach BI Developers in Analytics Engineering best practices and tools like dbt.
• Create documentation and playbooks for dbt projects.
• Advocate Analytics Engineering standards across BI and Data Engineering teams.

Drive self-serve analytics:

• Design data marts for self-serve usage with clear naming and reliable refresh cadence.
• Work with Insight Analysts to identify questions answerable via self-serve.
• Reduce ad-hoc data requests by providing trustworthy data.
• Ensure the semantic layer is AI-ready, clean, governed, and documented.