Designed, built, and optimized scalable data processing pipelines utilizing Python, PostgreSQL, and BigQuery, significantly improving system data accuracy and eliminating manual processing overhead

– Developed a battery pack analysis pipeline, reducing quality assurance time by ~30% and accelerating product release.

– Automated data migration and analysis from testing equipment to local file servers.

Developed event-driven serverless backend microservices using Cloud Run to process and auto-load large volumes of structured vendor data, unlocking real-time system monitoring capabilities.

Fortified distributed server infrastructure across multiple geographic deployments using Docker and Docker Swarm orchestration, ensuring high availability for critical data pipelines and reducing system downtime by ~18%.

Joined the Alexa Local Concierge team, utilizing APIs to provide restaurant reservation functionalities on Alexa devices.

Migrated core business logic from Alexa Conversations to Interaction Models to improve user experience.

Engineered a mocking testing framework using Java reducing integration testing failure rate by ~27%.

Built a Dagster-powered Python-SQL pipeline, tripling reporting speed.

Developed a Python Dash web form to customize battery test file configurations, reducing manual setup time and minimizing human error in testing workflows.

Designed and implemented a bookmarking system for Alexa Local Information using Java and DynamoDB.