QA Automation Engineer

Developed an Azure DevOps-integrated retry utility for failed automated tests, reducing manual re-execution effort by 40%.

Implemented browser automation utilities using Tampermonkey and bookmarklets to accelerate repetitive validation tasks and reduce manual testing effort by 30%.

RPA Developer

Consolidated five HRSSC tasks into a unified bot for checklist automation, reducing execution time by 40%.

Optimized legacy bots by removing redundant steps, reducing runtime by 70% and increasing throughput.

Business Outcomes

• Boosted AI response reliability by 20% by formulating a specialized validation methodology for benchmarking LLM outputs against delivery data.
• Performed automated and exploratory validation for predictive ROI and alignment scoring modules across complex role-based workflows.
• Designed custom Excel data calculation matrices to serve as the ground truth source, executing cell-by-cell data validation against automated database outputs.
• Owned end-to-end exploratory and functional testing of downstream impact modules, identifying critical edge-case logic defects before production releases.
• Generated detailed matrix reports and analytical defect data in collaboration with product managers, directly contributing to a 20% increase in AI response reliability.

Agility Insights(Agility Health)

• Designed and scaled a robust test automation framework using C#, .NET, and MSTest to validate data pipeline accuracy for the Agility Insights platform.
• Automated end-to-end verification of complex Agile delivery metrics, ensuring data integrity for sprint velocity, cycle time, and throughput dashboards.
• Integrated automated functional and regression test suites into CI/CD pipelines, reducing feedback cycles for platform updates from hours to under 15 minutes.
• Implemented data-driven testing models to validate diverse data schemas and high-throughput telemetry data feeding into the insights engine.
• Developed comprehensive automated API test suites to validate REST endpoints and ensure smooth data ingestion from Azure DevOps and Ideaboard integrations.
• Reduced test maintenance effort and false positives by 35% through modular reusable locator strategies and dynamic synchronization handling.