My Portfolio

Technical Implementation

• • Orchestrated containerized ELT pipeline using Apache Airflow and Docker to ingest real-time market data and financial news for S&P 500 equities daily
• • Architected serverless data warehouse on Google BigQuery, implementing idempotent loading strategies to ensure zero-duplicate data integrity
• • Integrated Google Gemini (GenAI) to process unstructured news text, engineering quantifiable sentiment scores to train a Random Forest predictive model
• • Developed an automated Looker Studio dashboard to visualize model performance and buy/sell signals, creating a closed-loop decision support system

Project Overview

FreightSwipe is a comprehensive logistics platform that modernizes freight matching through intuitive swipe-based interactions. The application connects shippers with available carriers in real-time, featuring advanced matching algorithms and comprehensive review systems.

Key Features

• • Swipe-based load matching interface for intuitive user experience
• • Real-time dashboard with live load updates and carrier availability
• • JWT-based authentication ensuring secure user sessions
• • Two-way review system building trust between shippers and carriers
• • RESTful API architecture for scalable service integration
• • Docker containerization for consistent deployment across environments

Technical Implementation

Built with React for the frontend user interface, Node.js and Express for the backend API, and PostgreSQL with Prisma ORM for efficient data management. The application implements secure JWT authentication and is fully containerized using Docker and Docker Compose for seamless deployment.

Impact & Results

This project demonstrates full-stack development expertise, combining modern web technologies with practical logistics solutions. The swipe-based interface improves user engagement while the real-time matching system enhances operational efficiency for freight management.

Project Overview

An advanced web scraping system designed for automated collection of product data from Walmart's e-commerce platform. The system handles large-scale data extraction with intelligent error handling and scalable database architecture.

Technical Features

• • BeautifulSoup and Requests for efficient web scraping
• • Intelligent retry logic with exponential backoff
• • Rate limiting to prevent IP blocking and ensure respectful scraping
• • SQLite database with optimized indexing for fast queries
• • PostgreSQL migration strategy for production scaling
• • Docker Compose configuration for modular deployment

Data Collection

The scraper collects comprehensive product information including product names, pricing data, customer ratings, availability status, and product descriptions. All data is structured and stored with proper normalization for analytical purposes.

Architecture & Reliability

Built with reliability and scalability in mind, the system implements comprehensive error handling, automatic retry mechanisms, and database optimization strategies. The containerized deployment ensures consistent performance across different environments.

Project Overview

A comprehensive sports team matching application designed as a capstone project to connect athletes with pickup sports teams. The platform facilitates seamless team formation and sports event coordination.

Development Approach

• • ReactJS frontend with responsive design for mobile compatibility
• • Firebase backend services for real-time data synchronization
• • Agile methodology implementation with structured Scrum meetings
• • Git version control for collaborative development
• • Comprehensive technical documentation covering design and validation
• • User authentication and profile management systems

Key Features

The application includes athlete profile creation, team discovery based on skill level and location, event scheduling, and real-time communication features. The platform supports multiple sports and provides analytics on team performance.

Project Impact

Successfully demonstrated full-stack development capabilities, project management skills, and the ability to deliver a production-ready application within academic constraints while following industry best practices.

Project Overview

Professional software engineering internship project focused on developing and deploying Python-based services for an e-commerce platform. The project involved automation of business processes and integration with third-party tools.

Key Achievements

• • Designed and deployed Python services in Docker containers
• • Automated Amazon repricing tools integration with Zoho One
• • Generated $6K+ annual cost savings through automation
• • Improved scalability and reduced setup time significantly
• • Collaborated with management on e-commerce platform features
• • Contributed to Agile ceremonies and technical documentation

Technical Implementation

Developed containerized Python applications with REST API integrations, automated data processing workflows, and comprehensive error handling. The solution integrated seamlessly with existing business systems while improving operational efficiency.

Business Impact

Delivered measurable business value through automation, reducing manual processing time and operational costs while maintaining high reliability and scalability standards in a production environment.