Bugs reported
512Defects identified across feature testing, regressions, releases, and edge cases.
Professional Impact
Quality work measured with evidence, not just activity.
My QA work combines manual depth, automation thinking, release support, test documentation, defect analysis, and cross-team communication.
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Bugs closed
474Defects validated, retested, and moved through closure with reproducible evidence.
Total tickets closed
427QA execution, validation, documentation, and delivery work completed across tracked tickets.
Story completion
99.3%Consistent story completion while supporting regression, release, and test documentation work.
Metrics are based on internal Jira reporting for the tracked review period.
Core QA strengths
- Manual and automated testing for web and mobile applications.
- Python-based automation with Appium and Sauce Labs exposure.
- Regression planning, release-night support, and production-readiness validation.
- Test plans, test cases, Confluence documentation, and QA traceability.
- Defect triage using logs, crash metrics, and reproducible evidence.
- Clear communication around blockers, priority shifts, and testing risk.
What makes my QA background useful for data roles
QA teaches a practical kind of systems thinking: how to question assumptions, find edge cases, validate outputs, communicate risk, and protect users from hidden failures.
In data science and ML work, the same mindset applies to data quality, leakage, bias, missing values, metric selection, model monitoring, explainability, and deployment readiness.
Representative achievements
Evidence-based examples of QA impact, delivery ownership, traceability, and quality leadership.
Defect discovery
Identified high-impact product issues before release
Reported 512 defects and supported closure of 474 through feature testing, regression cycles, release validation, and clear reproduction evidence.
Test coverage
Created extensive test documentation for new features
Authored 220 test cases out of 447 total new feature test cases, improving QA traceability, acceptance coverage, and clarity during development handoffs.
Automation
Supported automation in a high-volume QA environment
Worked with Python, Appium, and Sauce Labs and contributed 70+ Bitbucket commits while supporting regression testing, release validation, and technical QA workflows.
Data-driven quality
Used logs and crash metrics to support triage
Translated technical evidence into clear reports that helped teams prioritize issues, validate fixes, and understand product stability.