About
I graduated with a Bachelor of Science in Computer Science from the University of Arizona, focused on data engineering, machine learning, and building systems that make sense of large-scale real-world data.
Currently I'm at Meta as a Data Engineer in Reality Labs, based in the Bay Area. My most recent work includes architecting production data pipelines processing event-level data for 30M+ Meta Quest users, including automated anomaly detection for conversion rates and cohort-level behavioral analysis.
Before that I spent a year at Leidos as a Software Engineer on the Geospatial Intelligence team, contributing to the Commercial Joint Mapping Toolkit (CJMTK), a full-stack platform serving 5k+ monthly users at the U.S. Department of Defense. At the University of Arizona, I built ML-powered real-time occupancy forecasting for the Recreation Center using the Google Vision API and Scikit-learn, shipped as a live student-facing dashboard.
Outside of work I build things I find interesting — right now an iOS app that uses LLMs to convert unstructured personal health logs into structured, queryable data.
Work Experience
Meta · Data Engineer
Reality Labs · San Francisco Bay Area, CA
- –Worked to help optimize new user software flows in an agile, experimentation-driven environment, partnering closely with ML, data science, product, and engineering stakeholders.
- –Owned production-grade data infrastructure in Python and SQL at scale, with a focus on behavioral modeling, funnel analysis, and automated monitoring systems.
- –Translated analytical findings into product recommendations, presenting through design reviews and weekly cross-functional syncs.
Leidos · Software Engineer
Geospatial Intelligence Team · Tucson, AZ
- –Embedded on a defense-focused engineering team, contributing to a large-scale production platform under active use by U.S. government agencies.
- –Worked across the full stack in a code-reviewed, agile sprint environment — taking features from design to deployment with an emphasis on clean, maintainable code.
University of Arizona · Machine Learning Research Assistant
Electrical Engineering Department · Tucson, AZ
- –Conducted applied ML research under the Electrical Engineering Department, working end-to-end from raw data ingestion to live model deployment serving real students.
- –Iterated on model features and labeling strategies, balancing research rigor with the practical constraints of shipping a live, student-facing product.
Skills