15+ years of enterprise software quality experience across healthcare IT, financial systems, telecommunications, and cybersecurity. My background includes REST API testing, ETL and data validation, SQL-driven verification, requirements analysis, exploratory testing, and end-to-end system quality.
My portfolio brings that experience into hands-on projects covering healthcare APIs, data pipelines, browser and mobile automation, field investigation, and practical uses of AI in QA.
🔗 LinkedIn | 📧 jensen.md@gmail.com
| Project | Purpose and Approach |
|---|---|
| fhir-api-qa | Checks whether a healthcare API preserves Patient data through its lifecycle, with isolated FHIR tests and useful CI failure evidence. |
| pharmacy-spend-etl-qa | Checks that pharmacy spend data survives the trip from source files to analytics tables, using SQL reconciliation on a synthetic pipeline. |
| restful-booker-qa | Tests booking behavior at both API and browser layers, combining Postman/Newman and Playwright for repeatable regression feedback. |
| qa-automation-showcase | Examines API behavior and the quality of returned data together, using pytest, Postman, and Python validation. |
| android-appium-wdio-poc | Builds a focused baseline for Android UI checks, exercising Settings navigation on a local emulator with Appium and WebdriverIO. |
| mapmyrun-quality-investigation | Investigates how workout tracking affects data integrity and user trust, using mobile field experiments and separating observations from explanations. |
| ai-qa-framework | Explores how AI can help develop useful API test coverage while keeping a QA engineer responsible for review and pytest execution. |
| claude-code-qa-sessions | Shows how human QA judgment guides Claude Code from finding coverage gaps to making targeted, reviewed test improvements. |
| agentqa-orchestrator | Explores inspectable AI code reviews, using Gemini and Pydantic-defined response schemas to produce structured JSON audit artifacts. |
- Healthcare API and data quality: FHIR resource integrity, source-to-target reconciliation, and SQL validation.
- API and browser automation: focused regression checks with pytest, Postman/Newman, and Playwright.
- Mobile quality: local Android emulator automation and real-world iPhone/Apple Watch investigation.
- AI-assisted QA: structured test generation, repository analysis, human review, and inspectable artifacts.
Across these projects, I focus on isolation, clear assertions, repeatable execution, useful failure evidence, and conclusions supported by the work actually performed. Individual READMEs document scope, verified runs, and limitations.