Job Skill Tracker API (FastAPI + Postgres)

A personal “experience & skills” tracker built as a clean, testable REST API. The goal is to model job experiences, the skills used in those roles, and the relationships between them—so you can query, analyze, and grow a skills portfolio over time.

What this is

• FastAPI backend with a PostgreSQL datastore
• Designed around real-world API patterns: validation, persistence, relational integrity, and automated tests
• Built for extensibility (migrations, environment separation, CI readiness, Docker support)

Key capabilities

• Create and manage Jobs / Experiences
• Create and manage Skills
• Associate skills to jobs (many-to-many) with safe integrity rules
• Query and filter resources via REST endpoints
• Database schema managed through Alembic migrations
• Test suite with pytest (separate test database)

Tech stack

• Python, FastAPI
• SQLAlchemy (ORM)
• PostgreSQL
• Alembic (migrations)
• pytest (tests)

Architecture (high-level)

• API layer: FastAPI routes + Pydantic schemas
• Persistence: SQLAlchemy models + session management
• Migrations: Alembic for reproducible schema changes across environments
• Environments: separate .env (dev) and .env.test (tests)

Quickstart (local, no Docker)

Typical flow:

1. Create and activate a venv
2. Install dependencies
3. Create .env and .env.test
4. Create dev/test databases
5. Run Alembic migrations
6. Run tests
7. Start the API

Running tests

Tests run against the test database (configured in backend/.env.test):

export $(cat backend/.env.test | xargs)
pytest -v -s

Docker

This project is designed to work well with Docker.

Recommended approach:

• Use docker-compose to orchestrate services (API + Postgres)
• Use a Makefile as a convenient wrapper for common commands (dev up/down, migrate, test)

A typical Docker workflow will look like:

• docker compose up -d (start Postgres + API)
• docker compose exec api alembic upgrade head
• docker compose exec api pytest

Why this matters (engineering focus)

This project demonstrates:

• Proper modeling of relational data (many-to-many associations)
• Environment-safe configuration (dev vs test)
• Schema migration discipline to prevent drift
• Repeatable automation (tests, migrations, startup health checks)

Roadmap (next steps)

• Docker-first dev workflow (docker-compose.yml)
• CI pipeline (GitHub Actions): lint + tests + migration checks
• Repository/service layer refactor for larger scale
• Additional entities (projects, achievements, tags, metrics)
• Optional frontend (React) for dashboards and analytics

Screenshots / Demo

• To be added.

License

Private / personal project