Senior Product Quality Analyst · AI Tooling Builder
I studied business with a focus on operations and data analytics — which, in hindsight, was always about the same thing: finding where systems break down. Out of school I went into tech sales, and that's where I learned something QA school never would: the gap between what a team thinks they shipped and what a customer actually experiences can be enormous. That gap became my obsession.
I joined Meta in 2020 as a QA tester and spent 5+ years getting sharper at two things that complement each other well. The first is systems thinking — understanding where risk actually lives, building tooling that surfaces it early, creating feedback loops that make quality a team-wide property rather than a gatekeeper's job. The second is an eye for UX and design detail — I can tell when something is technically functional but experientially broken, and I built infrastructure at Meta to capture that signal at scale. The Golden Path program turned internal testers into structured VoC contributors, funneling qualitative product and design feedback directly back to eng and PM instead of letting it dissolve into Slack.
By 2024, AI made it possible to go further than just surfacing problems. I started building tools that help fix them — agents that score risk by surface, propose test coverage from diffs, generate fix candidates. The boundary between QA and engineering got interesting. I leaned in.
I'm most useful at the intersection of quality, operations, and engineering — roles where the job is to make a product more reliable, a process more defensible, or a system more observable. Especially drawn to teams building complex or high-stakes software where quality is a first-class concern, not a last-minute one.
AI nativity is non-negotiable at this point. I want to be somewhere that treats it as infrastructure, not a pilot program.