JobTrackerPro 🚀

"I stopped using Excel to track my job applications. I architected an Enterprise-Grade platform instead."

JobTrackerPro is a robust, full-stack solution designed to visualize, automate, and manage the interview pipeline. Unlike simple CRUD tutorials, this project demonstrates Advanced System Design, Cloud-Native Architecture, and Production-Grade Security.

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Who Is This For?

• Job seekers who want zero-effort tracking via email automation
• Engineers interested in real-world AI ingestion pipelines
• Developers learning enterprise Spring Boot + Angular architecture
• Open-source contributors looking for a non-trivial system to extend

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📌 Project Status

• JobTrackerPro is actively developed and open for contributions.
• The dev branch is the primary development branch.
• The main branch is reserved for stable, production-ready releases.

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🔗 Quick Links

• 🚀 Live Demo: https://thughari.github.io/JobTrackerPro
• 📂 Source Code: https://github.com/thughari/JobTrackerPro

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🧠 System Architecture

This application is built as a distributed system focusing on separation of concerns, data integrity, and automation.

1. 🤖 AI-Powered Email Ingestion Pipeline

I engineered an event-driven pipeline to eliminate manual data entry using Google Gemini 2.0 Flash.

• Smart Upsert: Uses fuzzy matching and Jaccard Similarity to detect if an incoming email is a new application or an interview update for an existing job.
• Resilience: Gracefully handles unstructured data, HTML-only emails, and missing headers using regex fallback strategies.

2. 🔐 Hybrid Security Architecture

• Implementation: Custom OAuth2SuccessHandler that merges identities based on trusted email verification.
• Flow: Users can log in via Google/GitHub OR Email/Password interchangeably without creating duplicate accounts or data silos.
• Stateless: Fully secured via JWT (RS256) with a custom Security Filter Chain.

3. ☁️ Atomic Cloud Storage

• Provider: Cloudflare R2 (AWS S3 Compatible).
• Transactional Integrity: Profile updates are atomic. If a user uploads a new image but the database transaction fails, the image upload is rolled back.
• Garbage Collection: The system automatically issues delete commands for old/orphaned images in the R2 bucket when a user updates their photo, preventing storage leaks and reducing costs.

4. ⚡ High-Performance Analytics

• Backend: Leverages Java Stream API for efficient in-memory aggregation of job statistics, reducing database hits to a single optimized read operation per dashboard load.
• Frontend: Uses Angular Signals for reactive state management and Optimistic UI updates, ensuring zero-latency feedback for the user even on slow networks.

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🛠️ The Tech Stack

Domain	Technology	Key Usage
Backend	Java 21	Modern JVM features (Records, Pattern Matching)
Framework	Spring Boot 3.4	REST API, Security, Data JPA
Database	PostgreSQL	Supabase managed instance (Transaction Mode)
AI Model	Gemini 2.0 Flash	Intelligent email parsing
Storage	Cloudflare R2	S3-compatible object storage
Frontend	Angular 17	Signals, Standalone Components, Optimistic UI
Styling	TailwindCSS	Utility-first styling, Dark Mode
DevOps	Docker & Cloud Run	Containerized serverless deployment

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📂 Project Structure

The repository is structured as a Monorepo:

JobTrackerPro/
├── backend/                # Spring Boot API
│   ├── src/main/java/      # Controllers, Services, DTOs
│   ├── src/main/resources/ # Configurations for local, dev, prod # use local for dev
│   ├── Dockerfile          # Backend Container Config
│   ├── scripts             # Utility Scripts (e.g., simulate-email.sh)
│   └── service.yaml        # Google Cloud Run Config
├── frontend/               # Angular UI
│   ├── src/app/            # Components, Services, Guards
│   └── tailwind.config     # CSS Configuration
├── .github/workflows/      # CI/CD (GCP Cloud Run & GitHub Pages)
├── docker-compose.yml      # Local Dev Infrastructure 
└── README.md               # Documentation

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🚀 One-Command Onboarding

You can run the entire ecosystem locally with zero configuration. The app automatically uses Mock AI and Local File Storage so you don't need any paid API keys to start contributing.

1. Spin up Infrastructure

Requires Docker Desktop. This starts PostgreSQL and MailHog (Email Trap).

docker-compose up -d

2. Start the Backend

cd backend
./mvnw spring-boot:run

3. Start the Frontend

cd frontend
npm install
npm start

4. Test the Features

• Dashboard: Access at http://localhost:4200.
• Emails: View outgoing emails at http://localhost:8025 (MailHog).
• AI Ingestion: Use the scripts/simulate-email.sh to see the Mock AI create jobs automatically.

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📸 Screenshots

Interactive Dashboard	Profile & Automation
Real-time D3.js analytics and charts	Email forwarding setup and secure settings

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🤝 Contributing

Contributions are welcome!

1. Fork the repository.
2. Create a Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request to branch dev.

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📄 License

This project is licensed under the MIT License.

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Designed & Engineered by Hari Thatikonda

Building scalable systems with Java & Angular.