Career Intelligence Dashboard

A data-driven platform that empowers tech professionals to make smarter career decisions through job tracking, skill analysis, and market intelligence.

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Table of Contents

• About the Project
• Key Features
• Tech Stack
• Folder Structure
• Development Stages
• Getting Started
• Environment Variables
• Contributing

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About the Project

The Career Intelligence Dashboard is a full-stack web platform designed for developers, tech professionals, and remote job seekers. It consolidates job application tracking, skill gap analysis, market trend visualization, and AI-powered career insights into a single CRM-like interface.

Rather than relying on spreadsheets or disconnected tools, users get a centralized, intelligent system that adapts to their career goals and the current job market.

Target Users:

• Software developers and engineers
• Tech professionals in data, design, product, and DevOps
• People pursuing remote or international career opportunities

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Key Features

Feature	Description
Job Application Tracker	CRM-style pipeline to manage applications from discovery to offer
Skill Gap Analyzer	Compare your skills against real market demand
Market Trend Dashboard	Visualize hiring trends by role, tech stack, and region
Career Insights Engine	Personalized AI-driven recommendations and career path projections
Resume Analyzer	Parse resumes and extract skills with AI matching

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Tech Stack

Layer	Technology
Frontend	Next.js 14 (React), Tailwind CSS, Recharts
Backend	Node.js with Express (REST API)
Database	PostgreSQL with Prisma ORM
AI/ML	Python (FastAPI microservice), OpenAI API
Auth	NextAuth.js / JWT
Deployment	Docker, Railway / Vercel
Data Pipeline	Python scripts, cron jobs, external job APIs

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Folder Structure

InteliCareer/
│
├── documentos/                  # All project documentation by delivery
│   ├── entrega1/                # Delivery 1: Foundation documents
│   │   ├── Big Data/            # Big Data concepts and strategy
│   │   ├── Cibersegurança/      # Security policies and threat model
│   │   ├── IA e ML/             # AI/ML strategy and tools
│   │   ├── empreendedorismo/    # Business plan and entrepreneurship
│   │   └── gestão de projetos/  # Project management and charter
│   ├── entrega2/                # Delivery 2: Backend architecture docs
│   ├── entrega3/                # Delivery 3: Frontend and analytics docs
│   └── entrega4/                # Delivery 4: Final product and AI docs
│
├── imagens/                     # Diagrams, wireframes, screenshots
│
├── src/                         # Source code
│   ├── backend/                 # Node.js/Express REST API
│   ├── frontend/                # Next.js application
│   ├── data/                    # Data pipeline scripts and collectors
│   └── analytics/               # Python ML/AI microservice
│
├── README.md
└── .gitignore

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Development Stages

Delivery 1 — Foundation (Weeks 1–2)

Conceptual planning, documentation, business model, architecture design.

• Project Charter
• Business Plan
• System Architecture Design
• Initial repository setup

Delivery 2 — Core Backend (Weeks 3–5)

Database schema, REST API implementation, authentication.

• PostgreSQL schema with Prisma
• Authentication system
• Core CRUD APIs for job tracker, users, skills
• Seeding scripts and API documentation

Delivery 3 — Dashboard & Analytics (Weeks 6–9)

Frontend development, data visualization, market trend charts.

• Next.js application
• Job tracker UI (Kanban board)
• Market trends dashboard with charts
• Skill gap visualization
• Connected to live backend APIs

Delivery 4 — Final Product (Weeks 10–12)

AI features, resume analysis, insights engine, deployment.

• AI-powered resume parser
• Career recommendations engine
• Full integration and polish
• Docker deployment
• Final documentation

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Getting Started

Prerequisites

• Node.js >= 18
• Python >= 3.10
• PostgreSQL >= 14
• Docker (optional, for containerized setup)

1. Clone the Repository

git clone https://github.com/your-username/InteliCareer.git
cd InteliCareer

2. Set Up the Backend

cd src/backend
npm install
cp .env.example .env
# Edit .env with your database and API credentials
npx prisma migrate dev
npm run dev

3. Set Up the Frontend

cd src/frontend
npm install
cp .env.local.example .env.local
npm run dev

4. Set Up the Analytics Service (AI/ML)

cd src/analytics
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8001

5. Run with Docker (Full Stack)

docker-compose up --build

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Environment Variables

Variable	Description
DATABASE_URL	PostgreSQL connection string
JWT_SECRET	Secret key for JWT tokens
OPENAI_API_KEY	OpenAI API key for AI features
NEXT_PUBLIC_API_URL	Backend API base URL
ANALYTICS_SERVICE_URL	Python AI microservice URL

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Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature
3. Commit changes: git commit -m "feat: add your feature"
4. Push and open a Pull Request

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Career Intelligence Dashboard — Built for professionals who treat their career as a product.