Spoorthi Basu

Spoorthi Basu

Software Engineer

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About Me

I'm a Software Engineer with 5+ years of experience building distributed systems that process data at scale: real-time pipelines, high-throughput microservices, and the infrastructure that keeps them reliable.

At Genesys, I've built large-scale Apache Flink pipelines that transform event streams into query-ready datasets on S3, scaled fault-tolerant Java and Kafka services, and engineered REST APIs handling 5K+ RPS with Redis caching that reduced database load by 50%. I've also delivered 99.99% uptime across multi-region systems and built organization-wide integration testing infrastructure from scratch, reducing test creation time to under a minute.

Recently published my first InfoQ article on schema proliferation in Kafka and Flink pipelines.

My focus is distributed systems, streaming architectures, reliability, and data infrastructure.

Technologies: Java, Spring Boot, Apache Kafka, Apache Flink, AWS, Redis, Docker, Terraform

Open to new opportunities and relocation.

Work Experience

Software Engineer - Genesys (March 2021 - Present)

  • Scaled fault-tolerant Java/Kafka microservices to 10M+/day via partitioning/rebalancing, cutting latency by 25%.
  • Flattened company-wide Kafka event streams via Flink into schema-driven, query-ready S3 datasets processing hundreds of millions of events daily and built a metadata-backed download service for secure, customer-facing analytics and self-service access.
  • Engineered 5K+ RPS REST APIs (Spring Boot/Redis) with cache sharding, reducing DB load by 50%.
  • Built org-wide integration testing 0→1 (LocalStack/AWS mocking), automating test creation to <1min for 5+ teams.
  • Delivered 99.99% uptime through multi-region deployment and circuit breakers (Resilience4j) for fault isolation.
  • Collaborated cross-functionally with Product and QA to deliver 10+ features, increasing daily active users by 25%.
  • Mentored junior engineer on distributed systems, halving onboarding time while maintaining 15-min P0/P1 SLA.

Genesys Website

Software Engineer - Coding Minds, Inc (July 2020 - February 2021)

  • Developed academic system (React/Java/Node.js) deployed on Heroku, serving 500+ daily active users.
  • Built RESTful APIs (Java/Spring) with MySQL CRUD operations, achieving 95% test coverage via Postman.
  • Led full SDLC from requirements to deployment using Agile/Scrum, delivering 3 major releases.
  • Enhanced React performance using memoization and lazy loading, improving page load speed by 30%.
  • Implemented automated testing for full-stack workflows, catching 20+ critical bugs pre-production.

Coding Minds Website

Latest Projects

Kafka Flink Schema Consolidation

Reference implementation for discriminator-based schema consolidation in Kafka and Flink pipelines

Kafka Flink Schema Consolidation

Reference implementation for discriminator-based schema consolidation in Kafka and Flink pipelines. Collapses twelve schemas into one consolidated Avro record, enabling single filtered queries over Apache Iceberg on S3. Companion to the InfoQ article on schema proliferation.

GitHub Link

Kafka Flink Audit Trail

Kafka Flink Audit Trail

Kafka and Flink pipeline that writes customer profile change events to an append-only Apache Iceberg table. Historical state is reconstructed at query time using SQL window functions, eliminating the need to store previous field values in the schema.

GitHub Link

Health Web

Health Web

Conceptualized, designed, developed and deployed the website on AWS for a clinic, enabling patients to view nearby doctors based on pin code and book appointments. Built with JavaScript, HTML, CSS, JSP for the frontend and Java for the backend, with MySQL via JDBC for data storage.

GitHub Link

Ecommerce Order Processing

Ecommerce Order Processing

Developed order microservices using Spring Boot, Spring JPA, Spring IOC, Spring MVC. Designed schema with PostgreSQL following normalization principles. Documented endpoints with Swagger, containerized with Docker, and used Log4j for logging and tracing.

GitHub Link

Hair and Skin Segmentation

Hair and Skin Segmentation using Machine Learning

Deep autoencoder using U-NET model for hair and skin segmentation with Keras, tested on the Celeb-A dataset. Includes data augmentation and transfer learning. Built with Keras, NumPy, and scikit-learn.

GitHub Link

Safe Driving

Safe Driving to Avoid Rear End Collision

Designed and developed a demo vehicle using Renesas Microcontroller with front and back ultrasonic obstacle sensors. Implemented proximity alerts via CubeSuite++ and Renesas Flash Programmer, with AWS storing copies of messages sent to nearby vehicles.