Prasanthi Dangeti
Professional Summary
QA Automation Engineer with 4.5 years of IT experience, including 3+ years building and maintaining Selenium WebDriver automation frameworks using Java and TestNG. Experienced in designing data-driven automation, executing functional and regression suites, performing root-cause analysis, and improving test stability in Agile environments. Delivered 200+ automated scripts for e-commerce and autonomous-vehicle data projects at Tata Consultancy Services. Google Cloud Associate Cloud Engineer certified and skilled at collaborating with cross-functional teams to accelerate release cycles and improve software quality.
Technical Skills
Work Experience
- Designed and maintained a Selenium WebDriver automation suite using Java, TestNG and Maven; implemented data-driven patterns and Page Object Model to improve script reusability and maintainability.
- Automated 200+ high-priority functional and regression test scripts for the OmniCart e-commerce application (checkout, cart, search, authentication) to accelerate release validation and reduce manual regression effort.
- Executed automated regression suites across multiple releases to ensure stability of critical flows and supported continuous delivery activities by improving test execution efficiency.
- Implemented cross-browser testing strategies with robust locator strategies and explicit waits to improve test stability and detect UI regressions across Chrome, Firefox and Edge.
- Performed root cause analysis on failed automation runs, created detailed defect reports and collaborated with development teams to triage and resolve issues, improving overall test suite reliability.
- Prepared sprint-level test plans, defined scope and exit criteria, and generated test execution reports and quality metrics for stakeholders to support release decisions.
- Performed image annotation and quality validation for autonomous vehicle datasets using Python-based annotation tools to create high-quality labeled data for ML pipelines.
- Executed annotation audits and quality checks to ensure labeling consistency, corrected metadata and maintained dataset integrity to meet model training standards.
- Used Python tooling to preprocess images, validate annotation formats and assist in generating training-ready datasets for downstream model teams.
- Supported virtual machine deployment, environment monitoring and operational tasks to ensure annotation infrastructure availability and performance.
- Collaborated with ML engineers and project stakeholders to prioritize annotation tasks, meet project timelines and maintain data quality standards.
- Documented annotation procedures, data issues and remediation steps to streamline handoffs and improve repeatability of dataset preparation activities.
Education
Certifications
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