Methodology

I hold a B.S. in Electrical Engineering and completed a Full-Stack Software Engineering immersive before moving into QA. This deep technical background is why I can read a codebase and understand the systems underneath. The knowledge of how an application works is the foundation I use to design stable test infrastructure and meaningful coverage. AI tooling extends the scope and speed at which I can work.

The method

  1. Learn

    I learn how an application works before testing it. I look at the UI, the database, the API calls, how data flows, and any external systems.

    Bank of America · 2024–2026

    At BofA the stack was Angular, .NET, and Kafka. A story was not finished until I had verified complex business logic against the UI, database, and Kafka topic messages.

    I knew the stack well enough to pick up small feature stories alongside my QA work.

  2. Build

    I build test infrastructure that doesn't affect real-world systems by mocking data and decoupling dependencies.

    Sovrn · 2022–2024

    At Sovrn, I built Playwright infrastructure in TypeScript that simulated ad slots on publisher sites so that auctions could be tested without affecting Google Ad Manager.

  3. Gate

    I wire test runs into CI so that quality can be enforced with minimal friction.

    Sovrn · 2022–2024

    I tooled the full CI pipeline using GitHub Actions, Webpack coverage instrumentation, and Datadog reporting.

    I simplified setup and seeding of the test framework to one command, enabling developers to run the tests locally.

  4. Set

    All framework decisions, reference tests, conventions, and UX patterns get codified into AI context.

    Bank of America · 2024–2026

    I wrote context and rules files for Copilot to generate manual test cases from acceptance criteria alone.

    This eliminated 80-90% of the repetivie and tedious work allowing me to focus on automation and edge cases instead of writing audit trails.

  5. Scale

    AI applies my work across the suite where I review every output against the original story or feature it was meant to follow.

    Bank of America · 2024–2026

    GitHub Copilot ported the remaining Playwright specs to the reference pattern, and drafted roughly 90% of a manual test case from a story's Gherkin acceptance criteria.