Reality Labs

Designed & built end-to-end infrastructure to track jobs performed within an extendable Iceberg-based data lake framework for storing large volumes of consumer data using SQS+Lambda architecture. Authored multiple API endpoints for core data operations, making these accessible via an API model and a scalable dashboard in which hugely reduced time to identify and redrive failed ETL jobs. Deployed above features via internal web app with module for setting security permissions based on deployment stage and user pools using AWS Cognito

Interned at Amplifi, a B2B social listening startup dedicated to collecting and presenting data from various social media pages like Facebook and providing businesses with software-driven sentiment analysis. Used RESTful APIs (Google Entities) and VADER text analysis to construct a multiple-language (English/Tagalog) machine learning module for commercial use, representing a ~25% improvement in accuracy for their primary classifier. Oversaw data visualization and financial projections in business case, presented material to company leadership, led to significant business decision to expand app capabilities to languages identified as financially lucrative, potentially representing $100,000 revenue growth within 5 years.