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Data Analysis & Automation
Turning messy, multi-source datasets into decision-ready insights with Python, SQL, and automated reporting pipelines.
In Practice
Where automation actually pays off
Good analysis is judged by the decisions it enables, not the code behind it.
I automate the repetitive parts of analysis (data cleaning, spatial overlays, report generation) so more time goes into interpretation and communication. My toolkit spans Python (pandas, NumPy, xarray, geopandas), SQL, and Streamlit for building lightweight internal tools that make repeatable workflows self-serve.
- Automated multi-layer overlay analyses in ArcGIS/QGIS, cutting data-processing time by 50%.
- Automated Google Earth Engine workflows to assess turbidity and land-water change using remote-sensing indices.
- Built a personal Streamlit dashboard automating hazard exposure metrics for distributed asset portfolios.
- Developed automated web scraping workflows using Python and Selenium to extract and structure data from online sources.
Methods & Tools
What I build with
Selected Evidence
Related case study
Other Areas