15+ years of enterprise software quality experience across healthcare IT, financial systems, telecommunications, and cybersecurity. Deep background in REST API testing, ETL and data validation, SQL-driven verification, requirements analysis, exploratory testing, and end-to-end system quality.
I build hands-on quality engineering projects spanning API automation, ETL and data validation, browser and mobile UI testing, exploratory investigation, CI/CD, and practical uses of AI in QA.
🔗 LinkedIn | 🐙 GitHub Profile | 📧 jensen.md@gmail.com
| Project | What It Demonstrates |
|---|---|
| android-appium-wdio-poc | Native Android UI automation proof of concept using Appium, WebdriverIO, TypeScript, and UiAutomator2 |
| mapmyrun-quality-investigation | Black-box mobile and GPS quality investigation using field evidence and bounded conclusions |
| restful-booker-qa | Layered API and UI automation using Postman, Newman, Playwright, and GitHub Actions |
| pharmacy-spend-etl-qa | ETL pipeline and SQL-driven data-integrity validation modeled after healthcare analytics work |
| qa-automation-showcase | REST API testing, data validation, and CI/CD-integrated automation |
| ai-qa-framework | Human-reviewed AI-assisted test generation with structured cases and pytest execution |
| claude-code-qa-sessions | Agentic analysis of existing QA repositories with human review and targeted implementation |
| agentqa-orchestrator | Structured agentic code auditing using Python, Pydantic, Gemini, and JSON |
The projects are intentionally different rather than variations of the same automation demo.
- API & backend quality: REST API validation, authentication, business rules, and automated regression
- Data quality: ETL pipelines, SQL reconciliation, structured validation, and healthcare-style analytics
- UI automation: browser-based end-to-end testing with Playwright
- Mobile automation: focused native Android UI automation with Appium, WebdriverIO, TypeScript, and UiAutomator2
- AI-assisted QA: test generation, repository analysis, structured LLM output, and human-in-the-loop validation
- Exploratory investigation: real-world mobile, GPS, sensor, and workout-record integrity testing
- CI/CD: repeatable automated execution through GitHub Actions
The common thread is using automation and evidence to understand whether a system is behaving correctly — and being explicit about what the available evidence does and does not prove.
Each project graphic uses a consistent visual encoding system. This section explains how to read it.
| Color | Testing Layer |
|---|---|
| 🔵 Blue | API / Backend / Services |
| 🟢 Green | Data / ETL / SQL |
| 🟠 Orange | UI / Frontend / Browser |
| 🟣 Purple | Infrastructure / Platform |
| 🟡 Gold | AI involvement |
| 💠 Cyan | Profile banner only |
- Bar height = depth of testing within that layer
- Bar width = volume of test coverage
- Number of bars = number of distinct system layers represented
| Position | Discipline |
|---|---|
| Dot 1 | Manual / functional testing |
| Dot 2 | API testing |
| Dot 3 | Data validation |
| Dot 4 | UI automation |
| Dot 5 | CI/CD integration |
| Dot 6 | AI involved |
A filled dot means that discipline is present.
| Ring Level | Meaning |
|---|---|
| Level 1 | AI-assisted development |
| Level 2 | AI in live testing workflow |
| Level 3 | Agentic AI workflow |
| Level 4 | AI-orchestrated workflow |
| Level 5 | Fully autonomous pipeline |
Rings fill from outside in. Hollow rings represent levels not currently demonstrated.