Staff Software Engineer

● Led enterprise-scale enhancements to Amazon Health, optimizing telehealth, pharmacy, and data platforms for millions of users, improving accessibility and patient outcomes.
● Developed backend microservices using Java/Spring Boot and Python/FastAPI to streamline Amazon Health’s virtual care workflows, reducing consultation setup time by 30%.
● Engineered secure RESTful APIs and GraphQL endpoints for Amazon Health, integrating with AWS IAM for HIPAA-compliant, multi-tenant patient data access.
● Integrated AI-powered anomaly detection models into Amazon Health’s monitoring pipelines using Python, TensorFlow, and AWS HealthLake, proactively identifying system performance issues.
● Developed NLP-based patient interaction analysis pipelines with FastAPI and PyTorch, enhancing Amazon HealthScribe’s clinical note generation accuracy by 35%.
● Architected scalable, event-driven services with Kafka and AWS serverless technologies, ensuring low-latency data flows for Amazon Health’s prescription and care coordination systems.
● Designed fault-tolerant microservices deployed via Kubernetes and Docker on AWS, enabling high availability across Amazon Health’s regional deployments.
● Built React.js and Next.js dashboards for real-time health metrics visualization, leveraging D3.js and Recharts to display patient and provider performance data.
● Created a responsive Angular portal with Tailwind CSS for Amazon Health’s self-service telehealth scheduling, reducing manual support requests by 25%.
● Automated infrastructure provisioning with Terraform and GitHub Actions, accelerating Amazon Health release cycles from weeks to days.
● Implemented observability stack using AWS CloudWatch, Prometheus, and Datadog, reducing incident response times by 45% for Amazon Health services.
● Mentored 4 engineers in cloud-native development, AI integration, and HIPAA-compliant practices, improving team velocity and code quality for Amazon Health projects.

● Contributed to the evolution of Snowflake’s cloud-native platform by engineering scalable, high-performance features that accelerated multi-cloud adoption and expanded enterprise customer usage.
● Engineered backend services in Java/J2EE and Python to optimize elastic compute scaling, improving query throughput by 45%.
● Built real-time ETL pipelines using Kafka, AWS Lambda, and SQL, reducing ingestion latency by 60% for petabytescale workloads.
● Developed React.js dashboards for real-time query visualization, leveraging Redux and WebSockets for low-latency updates.
● Modernized legacy dashboards by migrating AngularJS frontends to React + Redux Toolkit, cutting page load times by 30%.
● Architected cross-cloud storage integration with AWS S3, Azure Blob, and GCP Storage, ensuring consistent encrypted access across providers.
● Designed event-driven microservices with gRPC and C++, enabling secure, performant processing of distributed query workloads.
● Applied ML-based workload classification models to auto-scale clusters, optimizing compute resource allocation and reducing costs by 20%.
● Integrated anomaly detection pipelines using Python, scikit-learn, and Datadog, helping customers identify inefficient queries in real time.
● Built automated billing pipelines with Python + AWS Lambda, integrating Snowflake usage data with cloud APIs to reduce unnecessary expenses.
● Established Docker/Kubernetes staging environments with CircleCI for CI/CD parity, cutting deployment-related incidents by 35%.
● Mentored engineers in SQL optimization, microservices architecture, and cloud-native development, strengthening Snowflake’s culture of technical excellence.