Kenneth Barrett — QA and automation engineer. Messy input in,
verified output out
QA and automation engineer. I build the pipelines that collect, enrich, match, validate, and ship — and the test suites that prove each stage still works.
- Collectscrape · gatherSelenium · BeautifulSoup
- EnrichaugmentEnrichment APIs · TF-IDF
- MatchresolveFuzzy name + location
- Validatetest · verifypytest · XML · Verification APIs
- ShipdeployJenkins · AWS Lambda
Five systems, end to end
Each card shows where in the pipeline the work sat, and how far it went.
Contact enrichment pipeline
A Python pipeline that turns thin resident records into contact data you can actually trust. Selenium and BeautifulSoup collect from public directory sources, third-party contact enrichment APIs fill the gaps, fuzzy name and location matching resolves aliases and neighboring cities, and email and phone verification APIs confirm deliverability before anything reaches a caller.
XML transmission validation
The validation layer behind more than a million accepted tax returns. Built 700+ test cases with tax analysts spanning four taxing authorities, checked core calculations including the standard deduction against current law, and ran the regression pass that kept defects out of production.
Cross-platform automation suite
Took a drifting legacy suite and made it dependable again — 100+ deprecated pytest and Selenium cases rewritten against current behavior, new coverage across web, Android, and iOS, and full Jenkins integration so every build is verified automatically.
Infant MRI biomarker annotation
Annotation work supporting biomarker research into early autism diagnosis in infants, using structural MRI and functional MRI data. Built an iterative annotation process that raised accuracy and consistency across the imaging dataset — plus the team to run it. A KPI dashboard tracked productivity and quality daily, a TF-IDF model over QC notes turned scattered comments into patterns research scientists could act on, and I built and maintained the company website.
Virtual wig try-on
Led technical direction for a beauty-tech product and built its centerpiece — a Python face-swapping feature that puts a wig on the customer's own face before they buy. Deployed to AWS Lambda so it scales with demand rather than with infrastructure spend.
Six years, by domain
| 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | |
|---|---|---|---|---|---|---|---|
| Automation | Automation, 2020: light. | Automation, 2021: heavy. Selenium suites at Drake. | Automation, 2022: primary focus. 700+ cases, 4 authorities. | Automation, 2023: primary focus. Regression ownership. | Automation, 2024: heavy. | Automation, 2025: primary focus. pytest + Selenium rebuild. | Automation, 2026: primary focus. Jenkins CI integration. |
| Manual & QA | Manual & QA, 2020: light. | Manual & QA, 2021: primary focus. Exploratory + smoke testing. | Manual & QA, 2022: primary focus. Release verification. | Manual & QA, 2023: primary focus. Testing guidelines authored. | Manual & QA, 2024: heavy. | Manual & QA, 2025: primary focus. Web, Android, iOS coverage. | Manual & QA, 2026: primary focus. Defect tracking in Redmine. |
| Python builds | Python builds, 2020: primary focus. Face-swap try-on on AWS Lambda. | Python builds, 2021: light. | Python builds, 2022: light. | Python builds, 2023: light. | Python builds, 2024: moderate. | Python builds, 2025: primary focus. Contact enrichment pipeline. | Python builds, 2026: heavy. Suite tooling. |
| Data & ML | Data & ML, 2020: primary focus. TF-IDF on QC notes, KPI dashboards. | Data & ML, 2021: heavy. Infant MRI annotation program. | Data & ML, 2022: none. | Data & ML, 2023: none. | Data & ML, 2024: none. | Data & ML, 2025: moderate. Fuzzy matching + validation APIs. | Data & ML, 2026: light. |
| CI/CD | CI/CD, 2020: light. | CI/CD, 2021: light. | CI/CD, 2022: light. | CI/CD, 2023: moderate. | CI/CD, 2024: moderate. | CI/CD, 2025: primary focus. Jenkins pipeline integration. | CI/CD, 2026: primary focus. Continuous delivery workflows. |
| Leadership | Leadership, 2020: primary focus. Technical direction, Hairtelligence. | Leadership, 2021: primary focus. Managed annotation team. | Leadership, 2022: light. | Leadership, 2023: moderate. Trained new testers. | Leadership, 2024: heavy. Onboarded support hires. | Leadership, 2025: light. | Leadership, 2026: light. |
Languages & libraries
Testing
Tools & platforms
Experience
Sentact
- Perform automated and manual testing across web, Android, and iOS platforms to verify application quality
- Updated over 100 deprecated pytest and Selenium test cases to align with current application functionality
- Write and maintain new automated test suites using pytest and Selenium for cross-platform coverage
- Conduct exploratory, smoke, and regression testing to identify defects before production releases
- Track and document defects in Redmine while managing test cases and results in TestRail
- Integrate automated tests into Jenkins CI/CD pipelines to support continuous delivery
MHM Communities
- Built a Python pipeline to collect and enrich contact data for targeted community residents
- Scraped public directory sources using Selenium and BeautifulSoup to gather contact details
- Integrated third-party contact enrichment APIs to fill gaps and improve data accuracy
- Implemented fuzzy name and location matching to account for aliases and nearby cities
- Validated emails and phone numbers through verification APIs to confirm deliverability
Drake Software
- Assisted end users with technical questions for a DIY tax product, providing accurate and timely support
- Helped onboard new hires and assisted with escalated issues across the support team
Drake Software
- Validated XML transmission files tied to the transmission and acceptance of 1M+ tax returns
- Developed 700+ test cases alongside tax analysts to cover four taxing authorities
- Ran regression testing to catch defects before software updates reached production
- Tested core tax calculations, including the standard deduction, against tax law
- Created testing guidelines and trained new hires to keep testing standards consistent
PrimeNeuro
- Developed an iterative annotation process for infant MRI and functional MRI data, improving accuracy and consistency for biomarker research supporting early autism diagnosis
- Trained and managed a data annotation team, overseeing daily operations and data quality standards
- Built a KPI dashboard to track team performance, productivity, and data quality metrics
- Trained a TF-IDF model on quality control notes to surface actionable insights for research scientists
- Designed and built the company website
Hairtelligence
- Led technical direction and aligned development with digital marketing best practices to grow user engagement
- Built a face-swapping feature in Python for virtual wig try-ons, improving the user experience
- Deployed the feature to AWS Lambda for serverless scalability and cost efficiency
Education
University of the People
- President's List (current)
Bloom Institute of Technology
K12 International Academy
- Award for Excellence in Computer Science