Yashcodes
Backend Developer

Bhagvendra Singh PariharNode.js Backend Engineer Building Kafka, Redis & AWS Microservices

Backend engineer specializing in distributed systems, scalable SaaS architecture, Kafka event pipelines, Redis performance patterns, BullMQ workers, gRPC services, MongoDB, and cloud-native AWS backends.

Node.js·Kafka·Redis·Microservices Architecture·AWS (EC2, S3, Lambda, Cognito)·TypeScript·
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250+

Production APIs

10-15

Worker concurrency

20k+

Records processed

Profile

About

Backend systems engineering, production SaaS platforms, data processing, microservices, distributed systems, and AWS cloud infrastructure.

Focus Areas
Node.js
PostgreSQL
MongoDB
ETL/ELT
Microservices
AWS
SummaryProduction backend

Node.js backend engineer focused on scalable SaaS platforms, microservices architecture, distributed systems, and production-grade cloud backends. I build maintainable APIs with PostgreSQL-backed services, while also working across Kafka event pipelines, Redis caching and rate limiting, BullMQ background workers, gRPC services, MongoDB geospatial indexing, Amazon S3 presigned uploads, Amazon Cognito SSO, and AWS cloud architecture. My work spans 250+ production REST APIs, 30–50% backend performance gains, 1,000+ monthly payments, 500+ monthly roadside assistance requests, 40% dispatch accuracy improvement, and technical documentation that reduced developer onboarding time by 35%.

Focus

What I Build

The parts of backend engineering I spend the most time on: systems that stay clear, reliable, and easy to evolve as products grow.

Area

Backend systems

REST APIs, validation, service boundaries, and relational data models that keep product logic predictable.

Area

Data processing

ETL/ELT imports, tenant-aware transformations, batching, and memory-efficient background execution.

Area

Reliability & delivery

Queues, retries, observability, and cloud release flow that keep backend systems responsive under load.

Capability map

Technical Expertise

Core areas of backend and system design, expressed as production capabilities rather than a flat list of tools.

Service Design

Design APIs, service boundaries, and validation flows that stay maintainable as product scope grows.

Data Pipelines

Move large imports through queued, fault-tolerant worker pipelines with batching and tenant-specific transforms.

Relational Systems

Shape PostgreSQL models for reporting, exports, and transactional workflows with reusable access patterns.

Async Orchestration

Coordinate async work with queues and events while preserving fairness, retries, and predictable completion.

Logging & Diagnostics

Keep jobs and services debuggable with structured logging, traceability, and explicit failure states.

Cloud Operations

Ship backend services on AWS with Docker, CI/CD, secure managed services, and repeatable deployments.

Backend Development Experience

Professional roles building production backend systems, data workflows, APIs, microservices, AWS delivery, and engineering foundations.

Current role

Backend Developer | Node.js | PostgreSQL | MongoDB

Infinity Genesis Techso Private Limited

November 2024 - Present
  • Engineered backend workflows across 250+ APIs for appointments, workorders, inventory, dashboards, and multi-tier onboarding
  • Improved system performance by 30-50% through aggregation optimization, indexing, filters, and caching strategies
  • Built extensible export systems with Factory Pattern, PostgreSQL, Sequelize ORM, and Zod validation to keep tenant-specific export logic reusable and safe
  • Implemented MongoDB geospatial indexes and Amazon Location Service for roadside assistance routing
  • Improved mechanic dispatch accuracy by 40% and reduced wait times for 500+ monthly requests
  • Integrated Amazon S3 presigned uploads, Kafka events, Redis rate limiting, and gRPC services
  • Enabled compliant invoicing and 1,000+ monthly payments with Mexican CFDI + PAC stamping
  • Cut backend load by 40% through scalable API, caching, and async processing patterns
  • Standardized Winston logging and error tracing across APIs and background jobs for production support
  • Reduced new developer onboarding time by 35% through clear technical documentation
Previous role

Software Developer Trainee | Flutter | RestAPIs | Mobile App Development

DiracERP Solution Pvt. Ltd.

May 2024 - October 2024
  • Built WoodNapi, a Flutter-based offline app using GetX and sqflite with Node.js REST APIs for server-side data sync
  • Delivered 95% offline functionality while maintaining centralized data integrity
  • Architected Mavtra's real-time attendance system using Node.js and Express.js
  • Integrated face authentication and GPS-based location tracking
  • Improved attendance accuracy by 92% and reduced manual entries by 40%
Previous role

Web Developer | Frontend | JavaScript | HTML | CSS

AMH Communications Pvt. Ltd.

April 2022 - July 2022
  • Developed 10+ frontend templates including e-commerce, medical portfolio, and blog websites
  • Improved frontend development processes and team collaboration
  • Reduced project delivery time by 20% by applying implementation best practices
  • Enhanced implementation skills across HTML, CSS, and JavaScript
Quick reference

Engineering Stack

A compact reference for the stack I use most often in production. Kept light so the page stays centered on outcomes, not inventory.

Backend

Node.jsExpress.jsMicroservicesKafkaBullMQgRPCSSE

Databases

PostgreSQLMongoDBRedisSequelize ORMMongoose ODM

Cloud & DevOps

AWS EC2AWS S3AWS LambdaAWS CognitoDockerGitHub ActionsCI/CD

Architecture & Data Processing

ETL / ELTEvent-Driven SystemsQueue-Based ProcessingFactory PatternMulti-Tenant Systems

Observability

WinstonApplication LoggingError Tracking

Production Backend Case Studies

Node.js, PostgreSQL, Kafka, Redis, BullMQ, AWS, microservices, and distributed systems work.

Case studyProduction impact

Appviser ETL/ELT Pipeline for High-Volume Excel Imports

Problem

Appviser needed a reliable way to ingest large Excel files for multiple tenants without blocking the app, exhausting memory, or leaving users waiting blind during long-running imports.

Engineering approach

Designed a scalable ETL/ELT data processing pipeline using BullMQ and Redis for high-volume Excel imports. Processed 20,000+ records across concurrent worker jobs while applying tenant-specific transformation rules before persisting data into platform databases. Integrated ExcelJS streaming, SSE progress tracking, Winston logging, and fault-tolerant retry handling to keep batch processing memory-efficient and observable.

Case studyProduction impact

Scaling 250+ Production APIs

Problem

Multiple products and clients required a large set of stable, consistent REST APIs with clear ownership, versioning, and performance guarantees without sacrificing development velocity.

Engineering approach

Standardized API design (REST conventions, error shapes, pagination), shared middleware for auth, logging, and rate limiting, and a modular service layout so teams could own and deploy services independently while sharing platform capabilities.

Case studyProduction impact

Event-Driven SaaS Architecture with Kafka, Redis & gRPC

Problem

SaaS platform needed real-time updates, reliable async processing, and reduced coupling between services while handling 1,000+ monthly transactions and growing event volume.

Engineering approach

Introduced Kafka as the central message broker for domain events; services publish and subscribe instead of direct HTTP calls. Redis used for caching hot data, rate limiting, and session state to keep response times low and protect downstream systems.

Case studyProduction impact

Secure Multi-System Authentication with Cognito

Problem

Multiple applications and internal tools needed unified sign-in, SSO, and fine-grained access (20+ roles) without duplicating auth logic or compromising security.

Engineering approach

Centralized identity and access with Amazon Cognito: user pools for authentication, JWT-based API authorization, and role-based access control (RBAC) enforced at API gateway and service layer. SSO configured so users sign in once across systems.

System visualization

Bulk upload architecture as a live execution trace

A production-oriented view of request intake, BullMQ state, Redis Lua fairness, delayed retries, streaming S3 processing, event loop yielding, SSE updates, and job completion.

10

workers

2

per org

5s

delay

max 2 per org

Redis Lua Concurrency Gate

Workers acquire per-organisation capacity through an atomic Lua script; full organisations are moved to delayed state without burning retries.

atomic acquire
moveToDelayed +5s
0 attempts consumed

10-15

Concurrent BullMQ processors

2/org

Lua-enforced tenant fairness

0

Retries spent while capacity-blocked

Mentorship

Recognition

Contributions beyond shipping backend systems: mentoring, sharing context, and helping others build stronger engineering confidence.

0+

Students mentored in backend development

Recognition

Postman Student Expert

API and tooling expertise

Contact

Let’s talk

Open to backend hiring, architecture conversations, product work, and writing about engineering in public.