QA/ETL Test Engineer with Data Validation

Shivani Sara

Email: **************@*****.***

LinkedIn: linkedin.com/in/sarashivani/

Contact: +91-709*******

Location: Hyderabad, India

Professional Summary

Detail-oriented QA/ETL Test Engineer with nearly 2 years of experience in ETL Testing, Data Validation, API Testing, Web Application Testing, and Android Application Testing. Skilled in source-to- target validation using SQL and Python automation, with hands-on experience in Pandas and Datacompy for large dataset comparison and validation. Experienced in designing and executing test cases, functional testing, regression testing, smoke testing, API testing using Swagger, defect reporting, retesting, and collaborating with developers to resolve issues. Hands-on experience with Power BI, Azure Data Factory (ADF), Microsoft Fabric, and PySpark-based validation. Developed an AI/LLM- based POC to analyze automated validation reports for mismatch categorization, percentage-based analysis, pattern recognition, root-cause insights, and recommendations. Strong understanding of SDLC, STLC,

Agile/Scrum, and Data Warehousing concepts.

Technical Skills

ETL & Data Testing: ETL Testing, Source-to-Target Validation, Data Validation, Database Testing, Data Quality Testing, Data Reconciliation

Programming & Querying: SQL, Python, Pandas, Datacompy Application & API Testing: Web Application Testing, Android Application Testing, API Testing, Swagger, Functional Testing, Regression Testing, Smoke Testing, End-to-End Testing Automation & AI: Python Test Automation, Prompt Engineering, AI-Assisted Test Result Analysis Cloud & Data Tools: Azure Data Factory (ADF), Microsoft Fabric, PySpark, Power BI Tools & Platforms: ServiceNow, Excel

Professional Experience

QA/ETL Test Engineer

Amnet Digital – Hyderabad January 2025 – September 2026 Client: CDW

• Performed source-to-target data validation for ETL workflows and validated data movement between source and target systems.

• Executed complex SQL queries to validate transformed data, mappings, business rules, and data integrity between source and target systems.

• Developed Python-based automation scripts using Pandas and Datacompy to automate source-to-target data comparison and validation of large datasets

• Performed data quality checks including null validation, duplicate validation, data type validation, record count validation, and data integrity testing.

• Identified data mismatches and generated validation reports to document test results and support defect investigation.

• Gained hands-on exposure to Microsoft Fabric and PySpark-based data validation processes.

• Used Power BI dashboards for validation result visualization and reporting. Application & API Testing

• Designed and executed test cases and test scenarios for web applications and Android applications based on functional requirements and workflows.

• Performed functional, regression, smoke, and end-to-end testing across different application modules and business workflows.

• Documented test execution results and maintained test reports with execution status, defects, and validation outcomes.

• Identified and documented defects with clear steps to reproduce, expected results, actual results, and supporting evidence.

• Collaborated with developers to discuss API defects, clarify API requirements, verify fixes, and perform retesting to ensure the expected API behavior.

• Performed retesting and regression testing after defect fixes to ensure the changes worked as expected and existing functionality was not impacted.

• Participated in Agile/Scrum activities, including sprint meetings, requirement discussions, testing updates, and defect discussions. AI-Assisted Test Result Analysis POC

• Developed a Proof of Concept (POC) to enhance the Python-based data validation framework using Generative AI/LLM capabilities.

• Passed automated validation reports generated by the Python framework to an LLM for intelligent analysis and summarization.

• Designed prompts to categorize and summarize different types of data mismatches with percentage-based analysis.

• Used LLM-based analysis to identify patterns and recurring mismatch trends across validation results.

• Explored root-cause analysis of data mismatches by analyzing validation results and recurring error patterns.

• Generated actionable suggestions and recommendations to support investigation and resolution of identified data quality issues.

Project Details

• ETL Data Validation Automation Project

• Validated ETL mappings, transformations, business rules, and source-to-target data movement.

• Performed source-to-target comparison and data reconciliation using SQL, Python, Pandas, and Datacompy.

• Worked with Azure Data Factory (ADF) pipelines, Copy Activities, and Data Flows for ETL validation.

• Performed API testing using Swagger and validated requests, responses, status codes, and error scenarios.

• Developed automated validation processes and generated validation reports to identify and analyze data mismatches.

• Worked in an Agile methodology, managing user stories and testing activities, and collaborating with developers for defect identification, tracking, and resolution.

Education

• B.Tech – Computer Science and Engineering (CSE)

• Brilliant Institute Of Engineering And Technology 2023

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