Hi, I'm Debraj Dhar
Full Stack Engineer @ Nike
I architect and build scalable, high-performance web applications end-to-end. With 5+ years of experience at companies including Nike, Wakefit, and TCS, I specialize in React, Next.js, FastAPI, and cloud architecture. Passionate about optimizing critical systems and delivering exceptional user experiences.

About Me
I'm a full stack engineer passionate about building beautiful, performant, and accessible web applications. I have worked at leading companies like Nike, Wakefit, and TCS, where I spearheaded end-to-end architecture, led migrations, and optimized critical systems. I specialize in React, Next.js, FastAPI, and cloud architecture, with expertise in web performance, microservices, and modern development practices.
I thrive on solving complex problems across the full stack. From architecting scalable microservices and optimizing data pipelines to building responsive frontends and designing cloud infrastructure. Currently at Nike, previously at Wakefit and TCS, working on high-impact projects that serve millions of users.
Frontend
Backend & Tools
Specializations
- Microservices
- Cloud Architecture
- Monorepo (Nx, Turborepo)
Experience
Software Engineer
July 2025 – PresentNike
Spearheading end-to-end architecture and infrastructure optimization at Nike, focusing on orchestration pipelines and internal business platforms.
- Architected single-point orchestration pipeline enabling 7+ internal teams to manage large-scale data backfills
- Built PSA Airtalk - internal business chat query platform with React.js and FastAPI
- Accelerated data processing by 67% for 49M+ records via Jenkins CI/CD pipeline with parallelization
- Instituted reconciliation API ensuring 100% data completeness with eventual consistency
- Engineered AI testing workflow using Claude Opus, achieving 84% test coverage with automated pytest generation
Software Engineer
July 2024 – July 2025Wakefit
Led migration to modern stack and delivered core commerce features, significantly improving performance and user engagement.
- Directed Next.js migration (T3 stack: TypeScript, tRPC, NextAuth, Tailwind) delivering 10+ commerce capabilities
- Boosted engagement by 25% and retention by 15% through feature delivery (Wallet, Variant Edit, Infinite Scroll)
- Accelerated frontend performance by 45% - reduced TBT from 12s to 3s, achieved CLS under 1s via SSR, Redis caching, CDN optimization
- Revamped analytics infrastructure: integrated Strapi CMS, GraphQL across 60+ hierarchies, MoEngage & FB Conversion APIs for 50+ events
- Instituted Playwright-based TDD achieving 70% test coverage
System Engineer
May 2019 – Sep 2022Tata Consultancy Services
Developed full-stack solutions for enterprise clients with focus on monitoring systems and infrastructure management.
- Developed React JS monitoring dashboard for 100+ Azure SQL servers improving incident detection by 40%
- Administered 100+ SQL Server production/staging environments with clustering, failover, and backup strategies
- Maintained 99.9% uptime SLA for P1 incidents via automated health checks and failover protocols
Education
Master of Engineering (M.E.) in Software Systems
BITS Pilani
Bachelor of Technology (B.Tech) in Computer Science and Engineering
University of Engineering & Management (UEM), Kolkata
Featured Projects
Nike Data Orchestration Pipeline
Single-point orchestration system enabling multiple internal teams to trigger and manage large-scale data backfills.
Enabled 7+ teams to manage data operations, 100% data completeness guarantee
PSA Airtalk - Internal Chat Platform
Business query chat platform built with modern frontend and robust backend infrastructure.
Internal platform serving multiple business teams with real-time query capabilities
Wakefit E-commerce Migration
Complete migration to Next.js T3 stack with 10+ core commerce features and 45% performance improvement.
45% frontend acceleration, 25% engagement boost, 15% retention improvement
Azure SQL Monitoring Dashboard
Real-time monitoring dashboard for 100+ Azure SQL servers with advanced incident detection.
40% incident detection improvement, 60% reduction in manual monitoring effort