# Vishnu Vardhan Manivannan

> Backend Engineer | Go, Distributed Systems, AWS

Location: San Francisco, California, United States
Profile: https://flows.cv/vishnuvardhanmanivannan

I’m a backend-focused software engineer with experience in distributed systems and cloud-native architectures. I enjoy building reliable, well-structured services and working close to real production systems.

My core experience is in designing and scaling backend systems using Go, gRPC, PostgreSQL, Docker, and messaging systems like NATS and RabbitMQ, with a focus on performance, observability, and maintainability.

Previously, at HipBar (a consumer payments), I worked on multiple production-critical backend systems. I led the design and development of an in-house promotion engine that replaced a costly third-party dependency and reduced expenses by 72%. I also migrated core Python services to Go, improved authentication performance using Redis caching, and strengthened system reliability through better testing and observability.

Recently, I built GoDrive, a production-style cloud storage backend built with Go microservices and gRPC, supporting secure file uploads, metadata management, background processing with PostgreSQL and NATS, and S3-compatible object storage.

In parallel, I’m currently contributing to backend systems deployed on AWS as a volunteer software engineer, working with ECS-based deployments, IAM configurations, and CloudWatch for monitoring and issue diagnosis.

I’m most interested in backend-heavy roles that involve system design, scalability, and production reliability.

Portfolio: vishnuvardhanai.github.io

## Work Experience
### Volunteer Software Engineer @ Saayam For All
Jan 2025 – Present | San Francisco Bay Area
Contributing to backend systems deployed on AWS, implementing user-facing features and working within existing ECS, IAM, and CloudWatch infrastructure.

### Software Development Engineer @ HIPBAR
Jan 2019 – Jan 2021 | Chennai Area, India
1. Led the design and development of a fully in-house promotion engine, replacing an expensive third-party application and reducing revenue expenditure by 72%.
2. Reduced API latency and cut database reads by 40% by caching user authorization/identity metadata in Redis (RBAC + user context lookups).
3. Improved concurrency and runtime efficiency by migrating 33% of core Python microservices to Go using Gin and gRPC, including rewriting handlers and updating service workflows.
4. Improved scalability under load by introducing NATS/RabbitMQ-based asynchronous workers that offloaded notifications and event processing from the main request path.
5. Improved service-level observability by integrating Jaeger-based distributed tracing and Sentry monitoring across microservices, enabling clearer end-to-end request flows.


## Education
### Master of Science - MS in Industrial Engineering (concentration in Data Analytics)
University at Buffalo

### Graduate Certificate in Data Analytics and Machine Learning
Imarticus Learning

### Bachelor’s Degree in Electrical and Electronics Engineering
SRM IST Chennai


## Contact & Social
- LinkedIn: https://linkedin.com/in/vishnu-vardhan-manivannan
- Portfolio: https://vishnuvardhanai.github.io

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Source: https://flows.cv/vishnuvardhanmanivannan
JSON Resume: https://flows.cv/vishnuvardhanmanivannan/resume.json
Last updated: 2026-04-11