About
I'm a Berlin-based data analyst working across energy, renewables, geospatial analysis, and operational reporting. I like the stretch where a vague question becomes a dataset, then a checkable assumption, and eventually something people can use in an ordinary working session.
My background is SQL-first analytics, KPI reporting, Power BI, Python ETL, geospatial work, and data-quality validation. I have worked on reporting stacks where raw operational data had to become legible: energy KPI monitoring, weather-linked analysis, routing and location questions, and documentation that explains the choices people otherwise forget.
What I enjoy most is the full loop: clarify the metric, find the source of truth, clean what can be cleaned, test the parts that can break quietly, name what cannot, and build an output that is still understandable after the first result stops feeling new.
Current direction
Right now I am pulling my work into a sharper mix of:
- energy and renewables analytics
- forecasting-oriented analysis
- data engineering, data QA, and testing for analysis that needs to be rerun
- AI-assisted automation for review, documentation, and repetitive glue work
Geospatial analytics stays close to the center. Location, infrastructure, weather, and environmental signals rarely line up cleanly, which is exactly why I keep coming back to them.
Core toolkit
- SQL (PostgreSQL, DuckDB, BigQuery, Trino/Athena)
- Power BI (DAX, Power Query)
- Python
- ETL / ELT
- PostGIS / QGIS
- Excel
- Metabase
Currently exploring
- Forecasting systems
- Production-grade data workflows that are still small enough to reason about
- AI-assisted analytics automation
- Tools for energy, infrastructure, and environmental questions where assumptions matter