I build practical data and backend projects that are meant to be opened, run, and inspected. The work here is focused on SQL analytics, Python APIs, observability workflows, and reproducible project structure.
Live project site | Repository
40 runnable SQL analytics challenges with validation checks, learning paths, and browser-friendly challenge pages. The exercises cover cohorts, retention, windows, revenue analysis, and data engineering SQL patterns.
A FastAPI operations layer for pipeline run tracking, data quality checks, webhook alerts, alert delivery audit history, and a pipeline registry with owners, stale thresholds, and runbooks.
- Data engineering: ingestion, cleaning, SQLite/Postgres loading, and report outputs
- Python services: FastAPI, SQLAlchemy, migrations, API tests, and CI quality gates
- Analytics engineering: cohort analysis, retention, window functions, and growth metrics
- Full-stack applications: Next.js, TypeScript, Prisma, authentication, and CI-backed deployment
- Reproducibility: setup steps, validation workflows, and runnable local examples
| Project | Scope | Stack |
|---|---|---|
| Data Engineering Lab | Python pipelines that ingest data, load SQLite, and write report outputs | Python, pandas, SQL, SQLite |
| SignalBoard | Full-stack productivity dashboard with auth, database models, tests, and a live deployment | Next.js, TypeScript, Prisma, NextAuth, Vercel |
- Practice SQL analytics patterns: SQL Mini Challenges project site
- Inspect a Python API product: DataOps Observability API v0.2.0
- Review reproducible Python pipelines: data-engineering-lab
- Open the full-stack app: SignalBoard live deployment
Python | FastAPI | SQLAlchemy | Alembic | SQL | pandas | TypeScript | Next.js | Prisma | SQLite | Postgres | Docker | GitHub Actions