Bhumika Patil

Results-driven QA Engineer skilled in Manual Testing, Functional Testing, and Data Validation. Proficient in SQL, ETL validation, regression testing, and Python. Experienced in Agile/Scrum, ensuring 100% data accuracy and process efficiency. Seeking to apply testing expertise and deliver high-quality, scalable software solutions with precision and collaboration

QA Data Analyst - Manual & Database Testing

(2026-03 - 2026-07)

Sales Performance Dashboard Project

• Designed and executed Test Cases/Scripts on 10,000-row retail dataset, ensuring 100% accuracy across reporting modules
• Performed Functional Testing/Data Validation with SQL queries verifying sales by region and monthly revenue trends
• Executed Regression Testing post pipeline updates, confirming stability in pivot tables, charts, and dashboard filters
• Logged defects (duplicates, missing values) via Bug Reporting lifecycle, achieving zero critical defects at release
• Validated end-to-end ETL flow from raw data ingestion to dashboard output, performing Sanity Checks and Smoke Tests on each sprint deliverable in an Agile workflow
• Validated ETL flow from ingestion to dashboard, performing Sanity/Smoke Tests each sprint in Agile workflow
• Documented Requirement Traceability Matrix mapping business requirements to test cases, improving coverage and stakeholder confidence by 30%

QA Engineer - ML Model Validation & Testing

(2022-12 - 2023-05)

Flood Prediction ML Project

• Defined Test Scenarios and executed System Testing and Integration Testing for a flood prediction model across 3 ML algorithms (Random Forest, Decision Tree, KNN), validating model outputs against real-world rainfall datasets
• Performed Black-Box Testing by validating prediction results without internal model knowledge, cross-referencing outputs with historical flood records for Maharashtra territory
• Conducted data pre-processing validation through Feature Engineering verification to ensure input data quality and relevance before model training, eliminating data anomalies
• Validated ensemble (Bagging) model performance metrics, confirming the final model achieved an accuracy score of 0.912 — meeting the defined acceptance criteria
• Logged and tracked all model prediction discrepancies as defects, performing Root Cause Analysis and re-testing after algorithm tuning to ensure defect closure

BTech - Civil Engineering - JSPM Rajarshi Shahu College of Engineering, Pune (2019 - 2023)