Anudeep Chillamcherla

Stabilized and enhanced a banking analytics platform delivering real-time and batch insights via React + Next.js frontends and Node.js backend services. Designed backend services consuming aggregated data from Databricks via SQL Mesh pipelines, refreshed daily and surfaced through PostgreSQL materialized views for high-performance analytics queries. Implemented Prisma ORM over PostgreSQL and built hourly data refresh pipelines to ensure near real-time dashboard updates for business stakeholders. Built secure REST APIs and optimized backend workflows, improving API response times by ~30% and reducing query execution time by ~40%. Architected CI/CD pipelines and deployed cloud services to Azure Web Apps, enabling automated builds and zero-downtime releases. Contributed to an AI-augmented development workflow using Cursor and Claude, accelerating feature delivery while maintaining production quality. Collaborated with data engineering and frontend teams to deliver end-to-end analytics fea

Spearheaded development of an internal analytics platform integrating Angular, NestJS, PostgreSQL, and a Node.js API Gateway to provide real-time data insights for stakeholders. Designed interactive dashboards with Power BI integration, leading to 40% higher platform engagement and streamlined data accessibility for crucial business decisions. Optimized data models and queries, reducing analytical response times by 45% and enhancing overall system scalability to handle increased data volume. Fortified code quality and reliability by implementing 90%+ unit test coverage, simplifying long-term maintenance and reducing bug reports. Accelerated development cycles by 400% via integrating AI-driven tools like GitHub Copilot, streamlining coding processes, and improving developer productivity. Enhanced application security and performance tuning, contributing to a 30% reduction in production incidents and improving overall system stability.

Led the development of backend services using Node.js and GraphQL for efficient data querying in a banking application, reducing API call latency by 50%. Reduced overall execution time by 50% by identifying synchronous blocks and incorporating caching mechanisms like Redis. Integrated third-party services like AWS S3, Box, and Snowflake into the digital laboratory application, leading to a 90% increase in user adoption of integrated features. Enhanced team collaboration through active participation in sprint planning, user story estimation, and code reviews, achieving a 90% reduction in code defects. Optimized database queries in the banking application, identifying and resolving performance bottlenecks through SQL query optimization, resulting in a 75% improvement in database response time.