Senior QA Automation Engineer with AI-Driven Testing

Arun Sai G

Phone: 386-***-**** E-Mail: **********@*****.***

PROFESSIONAL SUMMARY:

4+ years of hands-on QA and automation experience across healthcare, telecom, and insurance domains.

Strong in functional, regression, API, and end-to-end testing for complex enterprise web and mobile applications.

Increasingly focused on AI-assisted testing approaches, including intelligent test optimization, model-based coverage, and automated validation techniques to improve test effectiveness and scalability.

Designed and maintained automation frameworks using Playwright, Selenium WebDriver, and Appium integrated with Azure DevOps and Jenkins pipelines.

Proficient in Python, Java, JavaScript, and SQL, developing reusable test utilities and validation scripts for backend and UI workflows.

Experienced in data-validation testing using Databricks, Azure Data Lake, and SQL to ensure accuracy across ETL pipelines and analytical dashboards.

Validated Power BI reports and dashboards by comparing KPIs against raw database queries, improving reporting reliability.

Skilled in API testing with Postman, REST-Assured, and SoapUI, verifying REST/SOAP endpoints for payload integrity and schema compliance.

Familiar with Azure Load Testing, JMeter, and K6 to measure API and DB performance under concurrent load conditions.

Developed and optimized automation frameworks following POM, Data-Driven, and Hybrid design patterns for scalability and maintainability.

Integrated test execution with CI/CD tools (Azure DevOps, Jenkins, GitLab CI/CD, GitHub Actions) for continuous validation and reporting.

Collaborated with data engineers and developers to validate schema changes, ETL outputs, and data-flow consistency across environments.

Experienced with Oracle, MySQL, PostgreSQL, and NoSQL databases for backend verification and query optimization.

Conducted cross-browser and mobile testing through BrowserStack and device labs, ensuring responsive UI behavior across platforms.

Practiced BDD using Cucumber/Gherkin, translating business requirements into clear, automated acceptance scenarios.

Active participant in Agile-Scrum ceremonies, defect triage, and sprint retrospectives; mentored junior QAs on automation and SQL-based data checks.

TECHNICAL SKILLS:

Testing Tools

Selenium WebDriver/IDE/GRID, Saucelabs, BrowserStack, Lambda Test, Playwright, Postman, SoapUI, Quality Center 8.2/9.0/9.2/10.0/11.0, SoapUI, UFT/QTP, K6, JMeter, Cypress, Metasploit, LoadRunner, PDFBox, PyMuPDF, Tesseract OCR,

Languages

HTML, CSS, Java, Python, JavaScript, C#, SQL, Ruby, Shell Scripting, TypeScript, Groovy, Faker.js

Test Framework

Robot Framework, Karate,JUnit, TestNG, PyTest, Appium,Cucumber

Build Tools

Ant, Gradle, Maven

Project Methodologies

Agile-Scrum, Azure DevOps, Kanban, Waterfall, TDD, BDD

CI/CD & Cloud

Jenkins, Azure /AWS, GitLab CI/CD, GitHub Actions, Databricks, Data Lakes.

Defect Tracking Tools

Jira, HP ALM, Quality Center, Bugzilla, TFS (Team Foundation Server)

Version Control Tool

GIT, SVN, GitHub, GitLab, Bitbucket

Web Services

RESTfull Services, WSDL, GraphQL API Testing,Swagger, Rest Assured, Postman, SoapUI, Mockserver

Databases

SQL, Oracle, MySQL, Teradata, PostgreSQL, DynamoDB, MongoDB, SQLite, NoSQL.

Operating Systems

Windows, MacOS, Linux (Ubuntu, CentOS), Unix

Test Management Tools

Quality Center, TestRail, Zephyr

PROFESSIONAL EXPERIENCE:

Client: HCA Healthcare Inc

Senior QA Automation Engineer Dec 2024 to Present

Responsibilities:

Designed and maintained end-to-end regression frameworks using Playwright (TypeScript) integrated into Azure DevOps CI/CD pipelines for continuous testing and nightly build validation.

Automated web modules related to patient registration, billing, and scheduling, validating workflows across Chrome, Edge, and mobile browsers.

Implemented mobile test automation with Appium + Java, executing suites on BrowserStack and in-house devices for iOS and Android coverage.

Extended QA scope into data-validation by verifying ETL outputs from Databricks Delta Lake and Azure Data Lake Storage, comparing source and transformed data using SQL and Python scripts.

Collaborated with data-engineering teams to review schema changes and ensure automated validation scripts remained accurate for evolving Databricks tables.

Verified Power BI dashboards by cross-checking visual KPIs with raw data queries to confirm reporting accuracy after nightly refreshes.

Developed reusable Python utilities to validate REST API responses against Databricks query results, enabling quick root-cause isolation of data mismatches.

Performed API testing with Postman and REST-Assured, validating authentication, payloads, and response codes across microservices.

Executed Azure Load Testing scenarios for key APIs and database queries, assisting DevOps with performance analysis and infrastructure tuning.

Conducted SQL (TOAD, PostgreSQL) validations to ensure synchronization between transactional databases and data-lake tables.

valuated AI-driven automation capabilities such as self-healing locators and automated failure analysis concepts to reduce flaky tests and improve long-term framework stability.

Integrated execution reports into Extent and HTML dashboards, linked with Azure Test Plans for traceability and management visibility.

Implemented AI-assisted validation using OCR-based techniques (Tesseract, PDFBox) to automatically verify dynamically generated clinical documents and reports for accuracy and compliance.

Participated in Agile-Scrum ceremonies, backlog grooming, and sprint retrospectives; collaborated closely with BAs and developers to refine acceptance criteria.

Managed Jira/Confluence artifacts, RTMs, and execution logs to maintain full traceability from requirements to deployment.

Supported smoke and sanity testing post-deployment, validating front-end, API, and cloud data integrations.

Assisted in cross-validation of patient-data pipelines between GCP Data Store and Azure Data Lake during phased migration.

Reviewed and optimized Playwright test suites, introducing smarter waits and error-handling to reduce maintenance overhead.

Partnered with QA leads and developers to triage defects, ensuring quick turnaround during release cycles.

Mentored junior testers on Playwright, API automation, and SQL-based data-validation methods.

Produced consolidated QA metrics and sprint-level reports for release sign-off and leadership reviews.

Client: British Telecom (Tata Consultancy Services)

QA Automation Engineer Jun 2022 to July 2023

Responsibilities:

Developed scalable Selenium WebDriver + TestNG automation framework following Page Object Model (POM) principles for telecom billing and customer-care portals.

Configured environment-specific builds via Maven profiles and managed cross-browser executions on Selenium Grid (AWS EC2) to ensure compatibility across Chrome, Firefox, Safari, and Edge.

Integrated automated regression suites into Jenkins pipelines, enabling nightly smoke runs and auto-generated HTML/Extent reports for release validation.

Practiced BDD using Cucumber + Gherkin, aligning automation scenarios directly with user stories and acceptance criteria.

Performed API testing with Postman and SoapUI, validating SOAP/REST endpoints, payload structures, and response times for service-layer integrations.

Collaborated with database and middleware teams to validate data mappings and transaction consistency using Oracle and MySQL queries.

Conducted responsive-UI and mobile compatibility testing through BrowserStack and internal test labs; verified layout stability and device-specific behavior.

Used Jira for defect logging, sprint tracking, and cross-team coordination with developers, business analysts, and release managers.

Contributed to functional, integration, and regression testing during system upgrades, improving defect detection rate and reducing post-release issues.

Participated in Agile-Scrum ceremonies daily stand-ups, sprint planning, and retrospectives to ensure QA readiness for every release.

Assisted in maintaining reusable Selenium utility classes and locator strategies to enhance framework stability and reduce maintenance effort.

Delivered weekly execution summaries and defect metrics to project stakeholders, improving release predictability and QA visibility

Client: Aditya Birla Sun Life Insurance

QA Automation Engineer Aug 2020 to June 2022

Responsibilities:

Designed and maintained automation suites using Selenium WebDriver + Java + TestNG, implementing the Page Object Model (POM) to reduce redundancy and improve script maintenance.

Automated regression and smoke tests for insurance policy, claims, and agent-portal workflows across Chrome and Firefox browsers.

Developed data-driven test cases using Excel (Apache POI) for large policy datasets; parameterized inputs for premium calculations and claim validations.

Performed API testing with Postman and SoapUI, validating REST/SOAP endpoints for policy creation, payment, and customer-information services.

Wrote SQL queries against Oracle and MySQL databases to verify policy data accuracy, payment transactions, and report generation results.

Supported manual testing for SIT and UAT cycles, collaborating closely with business analysts and developers to refine acceptance criteria and reproduce production issues.

Coordinated with the DevOps team to integrate regression runs into Jenkins pipelines, generating HTML/Excel reports for every build.

Implemented explicit and fluent waits to stabilize dynamic UI components and third-party integrations, reducing flaky test failures.

Validated backend data integrity during nightly ETL jobs; assisted in root-cause analysis of mismatched customer and payment records.

Logged and tracked defects through Jira and HP ALM, verifying fixes and maintaining traceability via RTMs.

Participated in Agile-Scrum ceremonies stand-ups, sprint reviews, and retrospectives—to ensure test readiness for each release.

Contributed reusable Selenium utility classes and common libraries that improved overall automation efficiency across QA teams.

Delivered weekly QA metrics and execution summaries to project stakeholders, demonstrating progress and highlighting risk areas.

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