Currently an Extern @Breaking Games
I turn messy data into meaningful insights, driving business decisions through SQL and database architecture.
Explore how I transformed messy data into a powerful analytics database and dashboard during my externship at Breaking Games.
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.
Engineered an end-to-end RAG pipeline to process a multi-document corpus of unstructured housing regulations and student data Implemented semantic vector search using all-MiniLM-L6-v2 embeddings with strict distance thresholds (<0.65) to enforce source attribution and eliminate LLM hallucinations
I turn messy data into meaningful insights, driving business decisions through SQL and database architecture.
I'm on the cusp of launching my career, currently gaining valuable experience through an externship in SQL and database architecture at Breaking Games. I thrive on transforming data into actionable insights.
Externships
SQL & Database Architecture Externship with Breaking Games
Breaking Games
Experience
AI Training Engineer
AfterQuery · Apr 2026 6 Jun 2026
AI Trainer
Handshake · Dec 2025 6 Present
Data Analytics Extern
Breaking Games · Jul 2026 6 Present
Education
University of St. ThomasSt. Paul, MN
Bachelor of Arts in 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 externship converted six raw CSV files into a local SQLite analytics database and produced SQL answers to five recurring revenue questions. Work included ingesting the source files, modeling a canonical dim_product table to align product identifiers, and iterating joined queries across sales and customer tables. The delivered outputs were five SQL queries, their result sets, and AI-assisted
I ingested six raw data sources into a local SQLite file, created a dim_product canonical product table to align records across sources, and wrote five SQL queries with result sets and AI-assisted validation notes.
I ingested six raw data sources into a local SQLite file, wrote SQL to answer five recurring business questions, and recorded the queries plus results and validation notes from an AI-assisted review.
Engineered an end-to-end RAG pipeline to process a multi-document corpus of unstructured housing regulations and student data Implemented semantic vector search using all-MiniLM-L6-v2 embeddings with strict distance thresholds (<0.65) to enforce source attribution and eliminate LLM hallucinations
Designed a 4-part agentic architecture (retriever, agent, evaluator, recommender) processing natural-language queries against a 10,000+ item catalog via REST Built reliability guardrails with structured logging and 42 unit tests, reducing hallucinated outputs by 91% with a 100% pass rate
Modeled a 5-table normalized SQL schema using SQLAlchemy ORM, cutting query times by 55% vs. flat-file architectures Authored 7+ RESTful Python/Flask API endpoints integrated with a React frontend