Experience & Education
2022 – present
Data Engineer II
Amazon Music
- Built and operated a 24/7 data quality monitoring system: petabyte-scale pipelines that process and monitor all 12B+ user events a day from user devices worldwide, detecting event volume drift and duplicate events and acting as the shield against critical data failures
- Built an aggregated dataset platform serving analytics and monitoring systems across Amazon Music, with HyperLogLog sketches (including a Spark–Trino sketch compatibility library), compressing 12B+ daily events into 200M records at ~1% error and making data queries 21x faster and 14x cheaper
- Built and operated a data pipeline and AI agent that triages ~10,000 ops tickets a week against a severity rubric I refined and generates personalized action items for all 100 teams, which cut tickets' mean resolution time from 30 days to 9 and lifted teams' high-severity assignment accuracy to 90%
- Led Amazon Music's org-wide table format move from Hive to Apache Iceberg: championed the switch, built the first production tables, and drove adoption across 10+ datasets, cutting partition migration from 7 days to 1 and metadata updates from ~2 minutes to under 10 seconds
2017 – 2022
Sr. Data Scientist
Oracle Cloud
- Owned training and inference pipelines for Oracle's Audience ranking ML service, including retraining and the production scoring path; improved performance 13% on average across quarters, driving more revenue
- Built a versioned offline feature store and training pipeline for ad-targeting models covering 200M+ US profiles and 1B+ credit card transactions a day
2016
R&D Intern
Tesla
- Vehicle diagnostics for Model S and X service
2015 – 2017
Master of Science
University of Washington
Big Data, Machine Learning, Data Science
2011 – 2015
Bachelor of Engineering
BMS College of Engineering
Signal Processing, Computer Networking, Electronics