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QA Automation Engineer with 10+ years in Data Quality & Manual Testing and 1+ year in Test Automatio
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Assessing your cultural and operational fit
Software Test Engineer with 8+ years of experience specializing in Data Quality and Manual Testing within healthcare environments. Expert in ensuring data integrity through anomaly detection and Root Cause Analysis (RCA). Proven track record applying Left-Shift Testing methodologies — validating quality from initial data ingestion to the final UI — to effectively mitigate business and compliance risks. Adept at collaborating in Agile/Scrum teams, managing defect lifecycles in Jira, and validating REST APIs with Postman. Currently expanding into test automation with 1 year of hands-on experience building end-to-end and API test suites using Playwright and TypeScript.
Colima University
B.S. · Software Engineering
N/A – June 30, 2026
Independent Practice
QA Automation Engineer
April 1, 2025 – Present
Mexico
Motivus / Agile Thought / 4TH Source (Client: Cigna)
Data Quality Analyst
January 1, 2016 – March 31, 2025
Mexico
ISTQB Foundation Level (CTFL)
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's background in healthcare data quality suggests a detail-oriented and responsible individual, which aligns well with roles requiring high accuracy and compliance. Their recent independent practice in automation indicates self-motivation and a desire to grow into the target role. The breadth of experience from manual QA to data quality and now automation shows a versatile skill set. The 'in progress' ISTQB certification further demonstrates commitment to professional development.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their experience in data quality analysis and root cause investigation. Their ability to optimize manual regression suites and develop standardized QA templates indicates a proactive approach to process improvement. Experience in Agile environments suggests good team collaboration potential. The transition to automation shows adaptability and a drive for continuous learning.