NITESH DHUNGANA/ SYSTEMS

Lead Backend Developer / Full-Stack Engineer · Kathmandu, Nepal

About Nitesh Dhungana

Nitesh is a full-stack developer with a strong backend focus. He builds scalable web applications, REST APIs, database-backed platforms, and dynamic frontends using Python, Go, C#/.NET, JavaScript, Django, React, and Vue. His experience spans multi-vendor commerce, logistics systems, CRM platforms, HR portals, educational assessment systems, real-time chat and notifications, and production deployment and infrastructure. His work emphasizes reliability, performance, clean architecture, and production readiness.

01 — Career timeline27.7172° N / 85.3240° E
  1. March 2025 — Present

    Lead Backend Developer / Team Lead · One Pasal

    Leads backend engineering for a multi-vendor commerce platform, from database architecture through deployment.

    • Leads the backend team for a multi-vendor platform
    • Oversees backend development and technical strategy
    • Designed the database architecture
    • Planned the overall project structure
    • Manages deployment processes and scalable infrastructure
    • Collaborates with cross-functional teams
    • Django
    • Python
    • PostgreSQL
    • Redis
    • Celery
    • Docker
    • Nginx
  2. July 2024 — March 2025

    Backend Developer · Susankya Global

    Built custom CRM systems and the backend for Kiwi Read, including SAT and PTE mock-test functionality.

    • Developed custom CRM systems
    • Built the backend for Kiwi Read
    • Implemented SAT and PTE mock-test functionality
    • Delivered full-stack applications using Django and Pug.js
    • Wrote unit tests for Django applications
    • Created dynamic interactions with jQuery and AJAX
    • Django
    • Pug.js
    • JavaScript
    • jQuery
    • AJAX
    • PostgreSQL
  3. November 2021 — July 2024

    Backend Developer · Gaaubesi Logistics

    Built delivery-management systems and HR portals, including real-time chat and notifications over WebSockets.

    • Built scalable applications using Django and Python
    • Developed delivery-management systems and HR portals
    • Worked with cross-functional product teams
    • Implemented real-time chat and notifications using WebSockets
    • Django
    • Python
    • PostgreSQL
    • WebSockets
    • Redis
02 — Education

  • 2017–2022

    Nepal College of Information Technology

    BE in Software Engineering

  • 2015–2017

    Golden Gate International College

    +2 Science

03 — Capability matrix

Application

Languages and frameworks used to build backend and frontend applications.

  • Python
  • Go
  • JavaScript
  • C#/.NET
  • Django
  • Django REST Framework
  • .NET Web API
  • Express.js
  • React
  • Vue.js
  • Pug.js
  • jQuery / AJAX

Architecture

How services, boundaries, and communication between them are structured.

  • REST API design
  • Authentication and authorization
  • Background processing
  • Real-time communication
  • Django Channels and WebSockets
  • Service and queue boundaries

Data

Persistence, modeling, and query performance.

  • PostgreSQL
  • MySQL
  • MongoDB
  • Database modeling
  • Query optimization
  • API optimization

Infrastructure

Where systems run and how they stay available.

  • AWS (EC2, S3, RDS)
  • DigitalOcean
  • Docker
  • Linux
  • Nginx
  • Gunicorn
  • systemd
  • Redis
  • Celery

Delivery

How correctness and change are verified before and after shipping.

  • Git
  • Django unit testing
  • pytest
  • Debugging
  • Database optimization
  • API performance optimization
04 — Engineering principles
  1. 01

    Design for failure, not only the happy path

    Systems are evaluated by how they behave when a dependency is slow, a queue backs up, or a request arrives twice — not only when everything works.

  2. 02

    Make system boundaries explicit

    Services, queues, and data stores should have clear contracts. Ambiguous boundaries are where incidents start.

  3. 03

    Measure before optimizing

    Performance work follows evidence — profiling and query plans — rather than intuition about where the bottleneck must be.

  4. 04

    Prefer understandable infrastructure

    A deployment a teammate can reason about at 2 a.m. is worth more than one that is merely clever.

  5. 05

    Keep architecture maintainable as the team grows

    Structure that works for one engineer should still make sense once several teams are shipping against it.

  6. 06

    Reliability is a product feature

    Uptime, correctness, and predictable behavior are part of what a platform promises its users — not a separate concern from the feature itself.