Data Warehouse Manager

Data Warehouse Manager
Bucureşti
Bucharest, Bucharest, Romania

Data Warehouse Manager

About the Role

Location Romania Bucuresti Bucharest Remote vs. Office Hybrid (Remote/Office) Company SIEMENS ENERGY Organization SE CFO Business Unit Wind Power Full / Part time Full-time Experience Level Experienced Professional

A snapshot of your day



We are looking for yearlong cover for our Data Warehouse Manager in our DPS team (Digital Processes and Systems). Helping to maintain, update and administer our FABRIC data warehouse.

How You’ll Make an Impact

Drive digitalization in Finance: Design and evolve robust data models (Lakehouse & Warehouse in Fabric) supporting Business Controlling and FP&A. Build automated data pipelines (Data Factory / Dataflows Gen2 / Notebooks) to replace manual Excel processes and reduce cycle time.

Automate and simplify finance processes: Refactor legacy workbooks into governed datasets and Power BI semantic models with clear definitions, KPIs, and lineage. Implement scheduling, monitoring, and alerting to keep pipelines reliable and simple to operate.

Enable Controllers with access, reporting & analysis: Deliver and maintain Power BI reports & dashboards (incl. DAX measures, RLS, deployment pipelines) that support variance analysis, trends, and
- through to transactions. Provide
- service data access with proper governance and documentation.

Run development work: Lead small
-
- end development tasks (requirements build test release) and contribute to larger
- company initiatives as the Fabric DWH.

Roll out digital solutions: Orchestrate releases to production, manage change, train key users, and ensure business continuity/support.

What You Bring:

Hands-on with Microsoft Fabric (One
Lake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2, Notebooks).

Strong SQL/T-SQL and data modeling (star schemas, medallion/layered design, slowly changing dimensions).

Power BI: dataset/semantic model design, DAX, RLS, incremental refresh, deployment pipelines, workspace governance.

ETL/ELT best practices, data quality checks, and job orchestration.

Clear documentation, stakeholder communication, and support mindset.