Flask API Projects Roadmap to Senior
Developer (with Data Science & Data
Engineering Integration)
This roadmap is designed to guide you from intermediate-level backend development to a
senior-level Flask API developer by December. The plan focuses on building scalable, datadriven API projects that also strengthen your Data Science and Data Engineering skills. Each
level builds upon the previous one, leading to a complete end-to-end project that
demonstrates production-ready capabilities.
� LEVEL 1 — Core CRUD & REST API (Foundations)
✅Time: 2 weeks
� Goal: Master API structure, routing, CRUD, database integration, and JSON handling.
**Project:** Student Performance Management API
- CRUD endpoints for students, courses, and grades
- Pagination, filtering, and searching
- Store data in SQLite/PostgreSQL using SQLAlchemy
- `/analytics` endpoint: mean, median, distribution, and top students
- Bonus: JWT authentication
� Skills: REST fundamentals, Blueprints, SQLAlchemy, DB schema design, basic analytics
� LEVEL 2 — Data-Powered API (Data Science Meets Backend)
✅Time: 3 weeks
� Goal: Build a data-driven service with Flask + Pandas + scikit-learn.
**Project:** CO₂ Emissions Prediction API
- Train a regression model to predict CO₂ emissions
- `/predict`: Accept car features and return emission prediction
- `/insights`: Return stats, feature importance, visualizations
- Add model versioning with `/model/info`
� Skills: Model serving, input validation, Pandas data handling, model serialization,
monitoring basics
🛠️ LEVEL 3 — Data Engineering + API (Real-Time & Batch Processing)
✅Time: 3 weeks
� Goal: Build APIs that integrate with data pipelines.
**Project:** Air Quality Ingestion & Analytics API
- `/ingest`: Ingest sensor data (JSON or CSV)
- `/stats`: Return statistics, trends, anomalies
- `/alerts`: Notify if thresholds exceeded
- Background ingestion jobs (Celery + Redis or Cron)
- Cache analytics responses
� Skills: ETL-style pipeline, async tasks, Redis caching, validation & storage strategies
� LEVEL 4 — Intelligent API Service (ML + Pipelines + Orchestration)
✅Time: 4 weeks
� Goal: Combine backend, data engineering, and ML in one microservice.
**Project:** Smart Recommendation API
- Ingest user behavior data and train a recommendation model
- `/recommendations`: Serve personalized suggestions
- Feature store implementation
- Automated retraining via API or schedule
- CI/CD pipeline integration
� Skills: End-to-end ML service, feature engineering, scheduled pipelines, CI/CD, testing
� LEVEL 5 — Capstone Project (Full Data Platform API)
✅Time: 4-5 weeks
� Goal: Build a production-grade data platform API.
**Project:** Climate Data Platform
- Data ingestion: CSV/JSON uploads
- ETL pipeline: Clean, transform, store data
- Analytics: Trends, anomalies, visualizations
- ML: Predict rainfall or emissions
- Pipelines: Airflow or Prefect automation
- Auth: JWT & RBAC
- Documentation: Full OpenAPI/Swagger
� Skills: Full-stack API, orchestration, CI/CD, scalable design, production logging & docs
� Weekly Roadmap (Now → December)
Month
Oct (Weeks 1–2)
Oct (Weeks 3–4)
Nov (Weeks 1–3)
Nov (Week 4) – Dec
(Week 2)
Dec (Weeks 3–4)
Focus
Flask fundamentals
Data + ML serving
ETL + Data Engineering
ML pipelines +
orchestration
Capstone + Deployment
Project
Student Performance API
CO₂ Prediction API
Air Quality API
Recommendation API
Climate Data Platform