Shantanu Bhusari
Backend engineer. I build distributed systems — microservices, event-driven pipelines, and real-time feeds that hold up in production.
Go · gRPC · AWS · Event-Driven Architecture
I'm a backend engineer who has designed and shipped production systems across e-commerce, fintech, and SaaS — 38+ microservices, 15+ Lambda functions, real users, real load. Most of the work is under NDA, but the architecture is mine to talk about.
I care about the decision behind the code. Why gRPC over REST for this service boundary. Why SQS is enough here and Kafka would be operational overhead. Why Fiber is the wrong framework inside a Lambda function — and how I learned that in production, diagnosed the cold start cost, and documented the fix. I try to know not just what to build but what not to reach for.
I'm open to backend engineering roles and contract work where architecture decisions actually matter — distributed systems, event-driven platforms, cloud-native infrastructure. If you're building at scale and want someone who has been there, let's talk.
Python
Node.js
Next.js
KotlinRequirements are constraints, not features
What does the user experience? What breaks if this is slow or down? That framing drives every decision — service boundaries, data model, failure modes.
Stack should fit where the product is
A monolith ships faster at phase one. gRPC and event-driven pipelines pay off at real scale — not before. I make the call that fits now, not what might be needed later.
Ship with measured confidence
Cloud services are decided before writing the first line. Production config comes from what testing actually showed — traffic patterns, latency, error rates — not guesses.
My Default Stack
Production topology — 15-service grocery platform. gRPC for internal latency-sensitive calls; SQS for async event distribution; Lambda for compute-heavy background work.
Full-Stack Software Engineer
- –Turned plain-English queries into live financial reports — NL2SQL pipeline (Gemini 1.5 Pro + OpenAI) lets non-technical users query production data without SQL knowledge, across a multi-tenant Go/React 19 platform.
- –Made email delivery a non-event: AES-encrypted BYOE SMTP + outbox pattern with MySQL SKIP LOCKED ensures zero message loss even when the downstream mail provider drops.
- –Shipped to GCP with zero downtime — Terraform-managed Cloud Run + Cloud SQL, deployed via GitHub Actions; infra changes go out without taking the platform offline.
- –Replaced manual report exports with PilotView BI Hub — users query, filter, and download CSV/XLSX/PDF in seconds; queries are validated against a schema allowlist before execution to prevent arbitrary SQL.
Lead Backend Engineer
- –Designed comms strategy across 35+ services: gRPC for latency-sensitive internal calls, SQS for async workloads — deliberately chose SQS over Kafka to keep operations manageable at this scale.
- –Traffic spikes on bulk order days used to spike compute costs — offloaded media transcoding and batch jobs to 15+ Lambda functions, keeping API response times stable without over-provisioning.
- –5,000+ order and payment events processed daily with zero message drops — explicit SQS state transitions (payment_confirmed → shipped → invoice_queued) made async fan-out to 4 downstream services reliable.
Backend Developer
- –Shipped 3 production SaaS backends in 14 months across automotive, fitness, and logistics — chose monolithic Node.js/MongoDB deliberately; the scale didn't justify microservices overhead at that stage.
- –Eliminated manual invoicing: automated PDF generation via Puppeteer + S3 replaced a fully manual process — clients went from hours of admin per week to zero.
- –Logistics queries were hitting 60s+; rewrote MongoDB aggregations with compound indexes, dropped to sub-10s — the slow queries were forcing over-provisioned compute, so fixing them cut cloud costs 60%.
B.Tech — Electronics & Telecommunication
- –Focus on distributed systems and algorithms
- –Built 10+ personal projects using JavaScript, TypeScript, Node.js, Express.js, and MongoDB
Kafka
PostgreSQL
DockerFrom Outage to Optimization: Stopping Cache Stampedes Dead in Their Tracks
How a thundering herd of cache misses can take down a production system — and the patterns (mutex locks, probabilistic early expiry, request coalescing) that stop them.
Go
RedisLet's build something together.
I'm open to full-time roles, freelance projects, and interesting collaborations. Reach out and let's talk.