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Sai Sangaraju
Senior Full-Stack Java Engineer specializing in FinTech and enterprise platforms
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Java/Spring Boot engineer with startup-style ownership experience across e-commerce, banking, and healthcare analytics. Stands out for driving a monolith-to-microservices migration with Kafka that improved checkout reliability under peak load, while also contributing full-stack with Angular and supporting production operations end to end.
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Key Strengths
- Hands-on Java backend development in production microservices environments
- Led migration from monolithic order processing to event-driven Kafka-based microservices
- Improved checkout speed and stability during peak e-commerce traffic
- Full-stack delivery across Angular front ends and Spring Boot back ends
- Production ownership including deployments, incident handling, and on-call support
- API and database performance tuning with indexing, pagination, and query optimization
- Built resilient distributed systems with retry handling, dead letter queues, and transaction boundaries
- Experience in regulated, high-reliability domains including banking and healthcare analytics
- Designed reusable, loosely coupled APIs for multi-system integrations
- Strong focus on data accuracy, auditability, and operational reporting
- Owned backend systems end-to-end from design through production operations
- Banking domain backend engineering in regulated, high-stakes environments
- Microservices modernization of legacy systems
- Strong API and data model design
- Security-focused backend design with Spring Security, JWT, and OAuth2
- Production observability and incident ownership
- Performance optimization with measurable impact
- Improved backend processing by ~35%
- Reduced query latency by ~40%
- Improved deployment cycle by ~30%
- Thoughtful tradeoff decisions across reliability, latency, scalability, and developer velocity
- Async architecture design using Kafka for fault tolerance and scalability
- Able to ship in ambiguous environments by defining MVP scope and validating assumptions early
- Pragmatic AI integration with strong controls around security, validation, and fallback behavior
- Prevention-oriented incident resolution with testing and monitoring improvements
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