Fullstack engineer on the Search Customer Experience Endorsements team.

Drove over $50M in incremental annual revenue by extending a Java backend service API to expose recommendation metadata and delivering a frontend tooltip experience using TypeScript, React, and Handlebars on the Amazon.com search page.

Improved customer trust and shopping outcomes, contributing to $67M in annual savings, by refining recommendation logic with new filters for frequently returned and defective products.

Built and maintained ETL pipelines to power recommendation features, using Python, PySpark, AWS Glue, Step Functions, S3, and DynamoDB to process and serve large-scale behavioral data.

Increased service resilience and regional redundancy by migrating an offensive query detection system to multiple AWS regions using AWS CDK and ECS.

Reduced infrastructure provisioning time by 70% by implementing infrastructure-as-code with AWS CDK (TypeScript) and Node.js, improving deployment reliability and scalability.

Established and led a structured operational review and delivery process, achieving 100% compliance with internal migration deadlines by creating a new backlog system and facilitating weekly cross-team planning and execution.

Improve quality-of-life and usability for over 90k customers by designing, developing, testing, and deploying new fullstack features for the SageMaker StudioLab ML/AI development environment.