Portrait of Subhra Manyu Das, senior software engineer
portfolio@github.io build: stable · JVM · CPython · tests in CI

Senior Software Engineer

Ships code. Owns the boring bits.

If it runs in prod, the design review was worth it.

I’m Subhra Manyu Das — a Bangalore-based senior engineer who works across services, data paths, release pipelines, production trade-offs, and test automation that earns its place in CI. B.Tech in Electronics & Telecommunication; I ship enterprise backends in Java and Python, own DevOps from commit to observable systems, and build automated checks — API, UI, and E2E — so releases stay boring. Outside the terminal: sketching and painting.

12+ yrs shipping 30+ projects IN Bangalore

Profile

  • Name Subhra Manyu Das
  • Practice Backend systems, APIs, DevOps, test automation & delivery
  • Style Senior engineer with a Tech Geek focus — systems, tooling, and how software behaves in production
  • Location Bangalore, India
  • Email subhramonyu.das@gmail.com

Capabilities

Stack & tooling

Backend-first, delivery-aware: I design, implement, test, and operate services that stay correct under load — and I treat pipelines, environments, automated tests, and observability as part of the product, not an afterthought. Comfortable across the JVM and Python ecosystems, DevOps and CI/CD, test automation (API, UI, E2E), and the glue between product and infrastructure.

  • Focus Java, Spring, Python, DevOps, test automation & platforms
  • Experience 12+ years
  • Delivered 30+ projects shipped
  • Toolkit GitHub Docker Jira Jenkins Terminal
Developer workspace — laptops and code on screen
Python

Core & advanced Python, scripting, data work, API development.

Django

REST, Django CMS, admin, caching, social auth, Guardian, Haystack.

Java

Java EE, core Java, data structures, JDBC, JUnit.

Spring Boot

Spring Data, JPA, Security, Thymeleaf, REST, microservices.

DevOps DevOps

CI/CD, containers, release automation, environments, monitoring hooks, and the habits that keep deploys boring.

Test automation Test automation

API, UI, and E2E coverage; JUnit, pytest, and browser automation; flaky-test discipline and CI gates that protect mainline.

Where I’ve built

Selected work

github.com/subhramonyu — experiments & code

Operating spectrum

Domains I operate in

A spectrum, not a skills matrix: each band is where judgment compounds — from infra and delivery to evidence, narrative, and models. Framed the way senior work actually shows up: cross-cutting, cumulative, and accountable in production.

12+ years in production
30+ projects shipped
07 domain bands
Domain spectrum 7 bands · tap to expand

Cloud & distributed systems

Systems that assume failure: regions, partitions, blast radius, and the discipline of designing for recovery instead of hoping for uptime.

  • AWS · GCP · Azure
  • Compute · object storage · serverless
  • EC2 · S3 · Lambda-shaped workloads

DevOps & release engineering

Merge → build → verify → promote → observe: pipelines that are boring on purpose, environments that match reality, and feedback that reaches you before the pager does.

  • CI/CD · Jenkins & ecosystem
  • Docker · Kubernetes-ready patterns
  • Test stages · gates · rollbacks

Application security

Threat-shaped defaults baked in early — identity, secrets, data in motion and at rest — so “security review” confirms design instead of rewriting it.

  • Encryption · hashing · auth
  • cryptography · bcrypt-class stacks
  • Least privilege · audit-friendly choices

Delivery & collaboration

Shipping is a team sport: clear ownership, visible work, and rituals that reduce surprise — Scrum or Kanban, always in service of flow, not paperwork.

  • Scrum · Kanban
  • Jira · backlog hygiene
  • CD habits · stakeholder alignment

Test automation, quality & observability

Automation where it counts: API and E2E suites, stable selectors, parallel runs, and CI gates — plus debugging and production signals when something still slips through.

  • pytest · unittest · JUnit · nose
  • API/UI/E2E · Selenium-class stacks
  • Logs · metrics · SLO thinking

Data analysis & visualization

From messy extracts to a narrative that holds up in a room — aggregation with rigor, visuals that don’t oversell, and reproducible notebooks where it matters.

  • NumPy · Pandas
  • Matplotlib · Seaborn
  • Explore → explain → defend

Machine learning

Classical ML with modesty: strong baselines, clean features, and evaluation you can explain — linear models, tree ensembles, and pipelines that don’t leak the future into the past.

  • Linear · tree ensembles
  • Feature & train/test hygiene
  • Metrics you’d stake a release on

Ping

Open a thread

Direct lines

+91 97425 43299

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