The Product Problem
Official California education data is powerful but fragmented across datasets, years, denominators, suppressed rows, and reporting rules. Simple rankings hide uncertainty and context, so Rui designed the product around transparent comparison rather than one-score judgment.
Rui's Role
Rui handled the product direction, frontend implementation, data modeling, reproducible Python processing, source validation, PostgreSQL canonical storage, and Cloudflare deployment path.
What Rui Built
- Context-aware comparison: school profiles, side-by-side compare, nearby schools, same-district references, and similar-context baselines.
- Trustworthy data layer: canonical facts, deterministic migrations, source notes, denominator visibility, suppression handling, freshness, and comparability caveats.
- Decision artifacts: family briefs and shareable comparison URLs that communicate tradeoffs without hiding methodology.
- Open-source product engineering: React, TypeScript, Python data tooling, PostgreSQL, and Cloudflare Worker Static Assets.
Capability Signal
This project shows Rui's strength at the intersection of data engineering, product judgment, frontend systems, public-data governance, and trustworthy analytics communication.