Contributed to the development of a geospatial data processing pipeline using Python and AWS services. Assisted in optimizing data ingestion and processing workflows, resulting in a 25% reduction in processing time. Collaborated with cross-functional teams to integrate machine learning models for predictive analytics.

Engineered a highly secure and scalable HIPAA-compliant backend for a maternal health application using Java Spring Boot and a serverless AWS architecture (Lambda, S3, DynamoDB, IAM). I focused on performance optimization by building and tuning distributed microservices in Java, which reduced API latency by approximately 30% through asynchronous design and non-blocking I/O. Additionally, I designed and enforced robust authentication and authorization flows using AWS Cognito and custom Java middleware to ensure strict data security and compliance.

Led a 5-member backend reliability team focused on modernizing core services using Java Spring Boot and SQL. Our efforts centered on systematic profiling and code-level optimizations, successfully improving data integrity by 35% and achieving a 15% reduction in query latency. I personally optimized database performance by refining indexing and query execution plans, resulting in 20% faster response times and 25% higher throughput in production. Furthermore, I decreased production incidents by 30% through targeted root cause analysis and resolution of recurring logic defects, significantly improving overall release stability.