Constructed scalable architecture with Python, AWS Lambdas, and Kafka to integrate with a third-party EHR API for processing millions of real-time clinical records. Increased efficiency of onboarding new clients by 20%.

Built data ingestion pipelines using Python and AWS Lambdas for ingesting 100M+ daily rows of patient and appointment data. Resulted in onboarding 3X more clients.

Replaced an expensive legacy product with a modern application for health system call agents. Participated in all aspects of fullstack feature development using React, TypeScript, GraphQL, and SQLAlchemy. Launched beta version in less than 2 quarters.

Facilitated seamless collaboration across departments to release new features while maintaining product usability across 15 major health systems.

Accelerated new customer onboarding by 50% through the implementation of internal dashboards with Vue.js and Axios, empowering sales teams.

Improved scalability of order and transaction system by architecting 2 new microservices, reducing bugs at critical points in the order lifecycle.

Maintained robust testing standards by writing Jest tests for GraphQL and RESTful APIs, boosting microservices test coverage by 70%.

Eliminated inconsistencies in transaction amounts charged to customers by creating a new data model.

Developed in-memory key-value data store inspired by Redis. Created core underlying LRU data structure that meets time-complexity benchmarks for O(1) operations.

Engineered a server matching Redis command API, ensuring seamless interoperability with Redis command-line interface.

Designed underlying data structures for storing 5 data types as values. Achieved time complexity parity with corresponding data types in Redis for operations.