
Breaking Games · ✅ Verified by Extern · ⏱️ In progress
SQL & Database Architecture Externship with Breaking Games
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
Currently an Extern @Breaking GamesI transform messy CSVs into structured databases and dashboards that inform decisions. Currently finishing a Breaking Games externship.
Portfolio items show data cleaning, schema design, query work, and a Q4 dashboard built during the Breaking Games externship using SQL and a writing assistant.

Breaking Games · ✅ Verified by Extern · ⏱️ In progress
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
I transform messy CSVs into structured databases and dashboards that inform decisions. Currently finishing a Breaking Games externship.
I’m an early-career data enthusiast learning SQL, data modeling, and dashboarding through hands-on projects. Currently completing a Breaking Games externship where I turned messy CSVs into an analytics database and a Q4 dashboard.
Externships
SQL & Database Architecture Externship with Breaking Games
Breaking Games
Skills
✅ Verified by Extern · ⏱️ In progress
Turn six messy CSVs into an analytics database and a Q4 dashboard that drives real business decisions. SQL + Claude.
The externship ingested six raw e-commerce CSVs, profiled their fields, and defined cleaning rules before mapping them into a relational analytics schema. The work set up a queryable SQLite database and produced a Q4 dashboard specification based on sample queries and the consolidated schema.

I audited six raw tables and produced five SQL queries that returned product and revenue answers, with query text and results recorded in a Week 1 Holiday Game Plan document.
I audited six raw tables, wrote five SQL queries that returned product and revenue answers, and recorded query text and results in a Week 1 Holiday Game Plan document for future weeks.
The project consolidated four spreadsheets into a unified schema, built dim_product with stable product IDs, joined ad, cart, and sales tables using INNER/LEFT JOINs, aggregated product performance with GROUP BY, and produced a reorder recommendation based on units and revenue.