About
I graduated with a Bachelor of Science in Computer Science from the University of Arizona, where I focused on software engineering, 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.
Work Experience
Meta · Data Engineer
Reality Labs · San Francisco Bay Area, CA
Aug 2026 – Present
1 mo
- –Build data pipelines and analytics dashboards evaluating how new Meta Quest windowing and panel features impact user behavior and system performance, including the movable, resizable app panels that enable PC-like multitasking in VR.
- –Own production-grade ETL workflows processing Quest behavioral and performance telemetry at scale, enabling teams to measure feature adoption and performance across diverse device and user segments.
- –Translate analytical findings into product recommendations, presenting through design reviews and weekly cross-functional syncs.
Meta · Data Engineer
Reality Labs · San Francisco Bay Area, CA
May 2025 – Aug 2025
3 mos
- –Partnered with engineering, data science, and product teams to design and deliver analytics infrastructure for the Meta Horizon mobile apps, tracking the end-to-end Quest headset pairing journey.
- –Built data pipelines and an interactive dashboard serving 30M+ Meta Quest users, enabling engineers to monitor step-level conversion rates, isolate drop-off points, and segment completion rates by device and demographic.
- –Integrated Bluetooth telemetry into a dedicated pipeline with automated alerting to detect statistically significant changes in pairing completion rates, enabling rapid incident response.
Leidos · Software Engineer
Geospatial Intelligence Team · Tucson, AZ
May 2024 – May 2025
1 yr
- –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
Dec 2023 – May 2024
5 mos
- –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.