Hi, I’m J. Law. Cordova.

I take software from estimate to production.

Senior developer and tech lead at Netzon in Davao City. I lead full-stack teams, design data platforms on Microsoft Fabric, and run releases with sign-offs and rollback.

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Front-end, class 1 of 7

Many hats. One craftsman.

These are the disciplines I work in, from Figma designs to production releases. I set up AI workflows that plan, build and check each change before it ships.

What I’ve been working on lately

Level Up Turn accomplishments into a game-style achievement list Oct 2026

Ship the gamified accomplishments section on my site

My portfolio now shows my accomplishments as a game-style achievement list, each with its own pixel-art icon, a playful name and a plain summary, and goals show as locked until they're done. It makes the section quicker to scan and more fun to read, with the full list on its own paginated page.
Command Post Manage accomplishments from the terminal Oct 2026
Built a command-line tool so AI assistants can manage my accomplishments

Claude Code can now list, add, update and remove my career accomplishments straight from the terminal, so it works even in setups that don't allow custom AI connectors. I built a small command-line tool with GitHub sign-in and one-step installs from GitHub Releases, backed by the same secured service the site already uses.
- CLI
- TypeScript
- Node.js
- OAuth
- Claude Code
- Cloudflare Workers
- GitHub Actions
- github.com (opens in a new tab)
Claude Code can now list, add, update and remove my career accomplishments straight from the terminal, so it works even in setups that don't allow custom AI connectors. I built a small command-line tool with GitHub sign-in and one-step installs from GitHub Releases, backed by the same secured service the site already uses.
Bedrock Designed a data lake keeping raw data Oct 2026
Designed a layered data lake with an untouched raw layer

Proposed a data lake design that keeps every incoming record exactly as received, sets aside bad data for review, and can rebuild downstream tables from the raw copy. This makes data problems fixable without re-requesting data from source systems. (Currently a draft in review.)
- Data Engineering
- Lakehouse
- Data Quality
- Architecture
Proposed a data lake design that keeps every incoming record exactly as received, sets aside bad data for review, and can rebuild downstream tables from the raw copy. This makes data problems fixable without re-requesting data from source systems. (Currently a draft in review.)
Moving Day Move legacy reporting onto Microsoft Fabric Not done yet