Senior Data Analyst | SQL, Python, data pipelines, and automation
I work with data after it leaves the source and before it becomes a report. Most of my day is SQL and Python: moving data between systems, checking it, automating recurring routines, and keeping the result easy to trace when something goes wrong.
I usually end up working on the parts that are easy to miss in a dashboard: deciding the order a process should run, handling dependencies, recording what happened, and keeping invalid records from quietly contaminating the output. When a question is analytical, I use business context and applied statistics to understand what the data is actually saying.
I am looking for Senior Data Analyst roles. My work is gradually moving toward analytics engineering, especially reliable datasets, repeatable pipelines, and analytical models that other people can use without rebuilding the same logic.
- LoL Draft Advisor — A Python tool that combines matchup data and applied statistics to suggest draft-aware builds for League of Legends.
- SUSEP Insurance Data Warehouse — A dimensional warehouse built from seven public insurance datasets, with ETL in SSIS and modeling in SQL Server.
- OMDb Movie Data Analysis — An exploratory analysis of 94,785 movie records, including resumable API collection, data cleaning, descriptive statistics, and visualization.
- Data development: SQL, Python, MySQL, SQL Server
- Data workflows: ETL, data pipelines, automation, API integrations, data quality, dimensional modeling
- Analytics and tools: Power BI, applied statistics, Pentaho, SSIS, Git