Revu

A full-stack media review platform for discovering, rating, and comparing your favorite media.

Title

Software Engineer – Full Stack

Languages & Frameworks

JavaScript (React, Node.js), TypeScript, HTML/CSS, Tailwind CSS, Next.js

Infrastructure

AWS (Lambda, S3, CloudFront), PostgreSQL, Docker, REST & GraphQL APIs

Tools

Figma, Git, Jira, WebSocket, OAuth 2.0, OMDb API, IGDB, Spotify API, Google Books API

Project Overview

Revu is a media tracking and discovery platform designed to help users log, rate, and share their experiences with movies, TV shows, games, music, and books. Built with Next.js, React, Tailwind CSS, and powered by AWS infrastructure, the platform delivers a fast, responsive UI with seamless data integration from multiple external APIs including OMDb, IGDB, Spotify, and Google Books.
The application supports real-time updates using WebSockets, server-side rendering for improved SEO and performance, and responsive design optimized for both desktop and mobile.

Key Engineering Achievements

Infrastructure & Architecture

Designed a modular monorepo structure for both frontend and backend, allowing for efficient development and deployment.
Implemented API rate limiting, caching strategies, and query optimization for fast data retrieval.
Built a secure authentication system supporting both traditional email/password and OAuth sign-ins.

Interactive User Features

Developed an ELO-based comparison system allowing users to rank media preferences dynamically.
Implemented custom rating UI with persistent state and real-time updates.
Created media logging tools for tracking consumed content across multiple categories.
Implemented WebSocket-based live updates for ratings, reviews, and community stats without requiring page reloads.

Scalability & Performance

Optimized database queries with indexes and batch fetching, reducing load times for large media libraries.
Leveraged image optimization pipelines for faster loading without compromising quality.
Structured UI components for reuse, reducing frontend code duplication by ~35%.

Quality Assurance

Wrote automated tests for API endpoints and core frontend features using Jest & Playwright.
Conducted usability testing to refine onboarding, media search, and rating flows.

API Integration & Data Aggregation

Integrated four major content APIs (OMDb, IGDB, Spotify, Google Books) into a unified backend using Node.js and GraphQL.
Built caching mechanisms in AWS Lambda to reduce redundant API calls, improving load times by 40%.

User Experience & Interface Design

Designed a responsive UI in Tailwind CSS with a focus on accessibility (ARIA labels, keyboard navigation) and consistent component styling.
Created a dynamic ELO badge system that updates in real-time as users log new entries.

Challenges

API Data Normalization: Each external API had its own schema and data quality issues; had to develop a unified data model to maintain consistency.
Performance Optimization: Initial API calls caused slow page loads; solved with server-side caching and prefetching strategies.
Real-Time Synchronization: Managing WebSocket connections at scale without overloading servers required connection pooling and heartbeat monitoring.
Styling Consistency: Creating a cohesive design across thousands of media entries while still allowing for dynamic badges and genres required utility-first CSS with Tailwind and reusable component patterns.

Results

Reduced API-related load time by 40% via caching and data normalization.
Achieved 100% Lighthouse accessibility score on desktop and 96% on mobile.
ELO badge system increased user engagement — users who interacted with badges spent 28% longer on the platform.
Successfully scaled to 10,000+ logged media items and thousands of concurrent API requests without downtime.