Currently an Extern @Breaking Games
I study Computer Science, I have practical experience, and I completed an SQL and database architecture externship with Breaking Games.
Projects include SQL and database architecture work from an externship with Breaking Games plus class and personal projects that test backend design and data modeling.
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 study Computer Science, I have practical experience, and I completed an SQL and database architecture externship with Breaking Games.
I am Esther Oginga, a second-year Computer Science student pursuing a bachelors degree. I have some practical experience and completed an SQL and database architecture externship with Breaking Games as part of my learning journey.
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 converted six messy CSV files into a structured analytics database and produced a Q4 dashboard. The work mapped source tables, documented data quality issues, and defined schema changes required for analytics. Deliverables included a cleaned relational schema and the dashboard-ready dataset.
I produced a cleaned relational schema for six source CSVs, documented data quality issues, and captured the SQL queries and five business 'instant answers' (top products by units and revenue, catalog size, total Meta ad spend, unique checkout sessions).
I documented the six data sources, pasted SQL queries I ran, and recorded five instant answers (top products by units and revenue, catalog size, total Meta ad spend, unique checkout sessions).
The project traced a scattered customer journey, created a dim_product primary key table, added foreign keys, and ran INNER and LEFT JOINs to produce a unified view linking clicks to purchases.
The project compiled campaign-level CTR, CPC, and ROI using Meta and sales data, reconciled 421 referral strings into a channel taxonomy, and produced a Holiday Q4 ad allocation plan recommending campaign actions and budget shifts.
The project examined checkout records, tested hypotheses about repeated customer bursts, identical subtotals, and extreme quantities, built filters to isolate bot-like sessions, and produced a cleaned abandonment rate recalculation.