Currently an Extern @Breaking Games
I am committed to building a solid foundation in Data Analytics and enhancing my resume for future opportunities.
🟢 Open to workDive into my portfolio showcasing my work in Data Analytics, SQL, and Database Architecture. Each project reflects my commitment to learning and growth.
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 am committed to building a solid foundation in Data Analytics and enhancing my resume for future opportunities.
I am a junior at Rutgers University pursuing a Bachelor's Degree in Computer Science. With some work experience under my belt, I'm eager to kickstart my career in Data Analytics and enhance my skills in SQL and Database Architecture.
Externships
SQL & Database Architecture Externship with Breaking Games
Breaking Games
Experience
Orientation Leader
New Student and Family Program · May 2024 - Aug 2026
Education
Rutgers University
BS Computer Science · Class of 2027
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 work ingested six disparate data sources into a working SQLite analytics database, inventoried raw files, and ran SQL to measure product and revenue metrics. It defined a dim_product table, joined sales and event tables, and aggregated revenue and units to produce a ranked product performance table. The work also cleaned 421 referral strings, calculated CTR/CPC/ROI per Meta campaign, and
I loaded six data sources into SQLite, built a dim_product table with keys and joins to event and sales tables, aggregated product revenue and units into a ranked reorder table, and cleaned referral data to calculate campaign CTR/CPC/ROI and a Q4 ad allocation plan.
On Day 1 I inventoried six data sources, loaded the provided SQLite database, ran SQL queries (top products by units and revenue, product count, total ad spend, unique checkouts) and recorded results in a Week 1 Holiday Game Plan document.
A fragmented set of four spreadsheets was unified by creating dim_product, joining it to event and sales tables, aggregating product revenue and units, and producing a ranked reorder recommendation table used to pick which products to restock.
I reviewed raw ad and referral data, created a channel taxonomy for 421 referrers, calculated CTR/CPC/ROI per Meta campaign, and produced a Q4 allocation plan showing which campaigns to continue, pause, or reallocate spend.
I reviewed checkout logs, tested hypotheses about bot patterns, identified scripted carts and a recurring bot cluster, documented data bugs (mismatched slugs, a catch-all referrer rule), and recalculated abandonment after removing noisy sessions.