Mid-level QA Analyst specializing in manual, automation, API, and backend testing
Saint Louis, MOQuality Analyst - Manual and Automation Testing4 years experienceMid-LevelTechnologyIT ServicesLegal
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About
QA engineer with 4+ years testing UX/UI for enterprise, data-driven and legacy platforms (including Dell Technologies), partnering closely with developers/product/business through Agile sprints. Experienced validating end-to-end behavior across UI, REST APIs (Postman), and databases (SQL), and automating regression with Selenium Java/TestNG. Notable work includes diagnosing search ranking/pagination issues by correlating UI behavior with API responses and metadata consistency.
Experience
Quality Analyst - Manual and Automation TestingDell Technologies
Quality Analyst - Manual and Automation TestingLexis Nexis
Education
Saint Louis Universitymaster, Information Systems (2025)
Key Strengths
End-to-end QA across UI, API, and database layers
Strong defect reporting with clear repro steps and evidence (screenshots/logs/videos/API comparisons)
Cross-functional collaboration to resolve root-cause disputes via live debugging sessions
Systematic test planning with RTM-based coverage and risk prioritization
Automation of stable regression flows using Selenium Java/TestNG with POM
Deep exploratory testing for edge cases (pagination limits, filter combinations, high-volume datasets, roles/invalid inputs)
Owned end-to-end automation covering UI visualization and backend/API validation
Improved coverage by adding pre-evaluation and backend data checks to catch production-impacting issues
Stabilizes Cypress suites via POM, stable selectors, custom commands, intercept-based waits, and mocking to reduce flakiness
Integrates automated tests into GitLab CI with headless runs and artifact-based reporting for fast feedback
Applies risk-based testing and test pyramid to prioritize API/service automation and keep UI tests focused on critical journeys
Strong defect isolation using multi-layer validation (UI + API + SQL/database) and clear, evidence-backed bug reports
Proactively influences quality early by clarifying requirements/edge cases and improving testability (data-test attributes, better errors/logging)
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