As a founding member of the greenfield Commercial Analytics Engineering team at Pfizer, I owned warehousing and migration of source of truth for 20+ Pfizer vaccine and drug portfolio brands including Covid/Flu, Oncology, Women's Health, Gastroenterology, and Rare Diseases by constructing ETL pipelines and generating datasets in a modernization initiative to increase delivery of business insights from Data Science machine learning models.

I built source-of-truth datasets for usage in Data Science machine learning forecasting models and executive dashboards, replatformed legacy ETL processes from Dataiku DSS (data science studio) into a modern data stack using Snowflake, Airflow, Docker, and Github Actions, while improving data quality, achieving significant reductions in cost, and delivering dataset enhancements. I validated and created brand-level datasets which consolidated pharmaceutical patient claims, sales and shipments, healthcare provider, and drug distribution data.

I shipped internal API tooling and rearchitected ETL pipelines and reduced runtimes of the heaviest workloads 50%+ and reducing snowflake compute from L to S/XS (8x reduction) for Pfizer's Oncology datamart, profiling inefficient processes in Dataiku DSS running on Spark, S3, EC2, and Snowflake, and replatforming onto AIrflow while refactoring queries and rearchitecting transformations achieving 75% reduction for both storage requirements by and cluster compute size, while implementing data quality checks and reducing data source latency from a monthly to weekly latency for executive dashboards.

Lead for Business Compromise in Cross-Meta Integrity (XI). Worked in detection, investigation, and recovery of compromised Business Manager clients to secure platform integrity for Meta’s 700MM+ advertisers. Developed automation tooling for investigation and recovery, ML pipelines and feature engineering, credit and payment risk frameworks for all Meta Business Manager clients generating $95B+ annual revenue - including all High Value Businesses ($80B ARR) and Small Spend Advertisers ($15B).

Data Engineering and Analytics: Developed and maintained critical data pipelines and frameworks used cross-functionally by Meta Integrity teams to triage, prioritize and recover compromised businesses and for topline metrics and goalin. Owned and maintained primary source-of-truth critical infrastructure datasets and Business/User Graph relationship datasets, deployed ML models and created feature engineering datasets for machine learning detection classifier models.

Data Stack/Tooling: Hive, Spark, Presto/Trino, MySQL, Python, Dataswarm (Airflow), Bento (Juypter), Tableau/Unidash/Daiquery (SQL IDE + Dashboarding tools), Scuba (Grafana equivalent), Loggers, Backfill

Software Engineering: developed Autopilot, a system to automate point-of-compromise detection and Automated Investigation and Recovery (AIR). These were internal tooling and infrastructure launched as central tooling for Business recovery workflows used quarterly on tens of thousands of businesses, including multi-million dollar High Value Business clients. Performed full-stack work with backend focus: primarily product tooling, logging, and integration with Data Warehouse, and some UI development.

Tech Stack/Tooling: Hack (PHP), EntQL (GraphQL wrapper), React/Javascript, chronos Python ETL, Scuba (in-memory realtime analytics DB), Laser (in-memory key-value store).