Experience & Education
2022 – present
Data Engineer II
Amazon Music
- Ensured pristine quality of all user behavior data across 100 million+ users globally, processing 12 billion+ events per day
- Built the data quality control platform adopted company-wide for DQ checks — as one example of impact, caught issues that could have resulted in $$$ overpayment of royalties
- Cost optimized the platform by reducing data movement and tuning compute clusters for full utilization
- Built an ML based app version dial-up monitoring system tracking events per user to detect anomalies during bi-weekly version rollouts across both iOS and Android devices, preventing 2 major iOS app version issues before global dial-up reached 5%
- Built the HyperLogLog Sketch user events dataset compressing 12 billion user events per day to 200 million sketched records, enhancing query performance by more than 10x
- Built the code library to create HyperLogLog Sketch aggregation in Spark compatible with Athena (Trino) HyperLogLog implementation, used by the whole organization to create other HyperLogLog Sketch datasets
- Built LLM-optimized dataset with semantic layer for internal Text-to-SQL product
- Created an AI/LLM Operational Excellence tool used by all Amazon Music employees across 60 teams to drive their operational excellence
- Led the adoption of Iceberg tables at Amazon Music — built the first Iceberg tables which drove adoption to 10+ datasets across data infrastructure, migrating teams from Hive to Iceberg and still expanding
2017 – 2022
Sr. Data Scientist
Oracle Cloud
- Developed and operated a machine learning service for Oracle's Audience ranking products, improving algorithm performance by an average of 13% across multiple quarters, resulting in better benchmarks and more revenue
- Built the feature engineering system for ad targeting of 200 million+ US individual profiles, processing over 1 billion credit card transactions daily
- Saved the company ~$400K by migrating the demographics data system used for machine learning to a system built on open source libraries
- Collaborated with cross-functional teams to integrate measurement products, driving data-driven decision-making
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