Open to opportunities
software engineer building data pipelines
5+ years turning messy external data into clean, validated records — with TypeScript, React, Node.js, Python, and PostgreSQL doing the heavy lifting.
Integration problems, solved end to end.
What I bring to a team
The languages, frameworks, and platforms I reach for most often.
- Languages
- TypeScriptJavaScriptPythonJava
- Frontend & Mobile
- ReactReact Native (Expo)Next.js
- Backend & Data
- Node.jsFlaskSpring BootPostgreSQLMySQLLiquid Templates
- Platforms & Tooling
- ElectronDockerGitVitest / Testing Library
Latest posts
Notes from recent work — debugging, tooling, and the occasional deep dive.
109 Focused Tests Passed. The Final Video Still Wasn't Proven.
A green test suite did not prove that the final MP4 still matched the approved package. The repair binds the current video path and SHA-256 digest to the validation report.
The Identity Check This AI Video Pipeline Was Missing
The publish gate now requires the reviewed package, story, current video path, and current bytes to agree, stopping stale state or the wrong artifact before release.
The Prompt Constraint That Kept This GPT-5.6 Sol Refactor Bounded
One behavior, three integration boundaries, 12 application files, and no merge authority: the constraint that kept a real GPT-5.6 Sol refactor narrow enough to review.
I Gave GPT-5.6 Sol a Real Repo Refactor
A bounded GPT-5.6 Sol refactor inside a real production video system, followed by an independent review that found the missing artifact-provenance guarantee.
Your Local AI Tools Should Train From Their Logs
Local assistants already record failures, retries, repairs, and corrections. With training events, redaction, quality gates, and evals, those logs can become a disciplined learning loop.
/ Let's build
Open to opportunities.
Interested in software engineering roles focused on data integration, developer tooling, or cross-platform product work — especially where data accuracy and reliability are non-negotiable.