Job Summary

Key Responsibilities

QA Strategy & Technical Leadership

• Lead the overall QA and test automation strategy for enterprise applications and services.
• Define risk-based test approaches covering functional, API, integration, end-to-end, regression, smoke, data validation, and performance testing where applicable.
• Establish quality standards, reusable automation patterns, coding practices, review standards, and measurable quality gates.
• Provide technical leadership and mentoring to onshore and offshore QA engineers.
• Participate in architecture and solution-design reviews to identify testability, observability, dependency, and quality considerations early in the SDLC.

Automation Engineering

• Design, develop, and maintain scalable, reusable, and maintainable test automation frameworks.
• Automate testing for Java, Spring Boot, Node.js, Python, REST APIs, microservices, and web applications as required.
• Select automation tools and frameworks based on architecture, testing level, team capability, maintainability, and delivery needs rather than prescribing a single tool.
• Build reusable test utilities, fixtures, mocks, data builders, reporting components, and common libraries.
• Improve automation coverage while reducing brittle tests, flaky execution, excessive maintenance, and unnecessary UI-level automation.
• Apply sound software-engineering practices to test code, including modularity, version control, peer review, error handling, and maintainable design.

API, Integration & Microservices Testing

• Develop automated tests for RESTful APIs, microservices, and service-to-service integrations.
• Validate request/response payloads, HTTP behavior, authentication and authorization, business rules, error handling, data integrity, and downstream dependencies.
• Implement contract, component, integration, and end-to-end testing at the appropriate layers.
• Support testing of synchronous and asynchronous integration patterns, including event- or message-driven flows where applicable.

UI & End-to-End Automation

• Develop automated end-to-end tests for critical web workflows where UI automation provides appropriate value.
• Use modern UI automation technologies such as Selenium, Playwright, Cypress, or equivalent tools based on project needs.
• Favor a balanced automation strategy that places tests at the lowest reliable layer and avoids over-reliance on brittle UI tests.

AI-Assisted Quality Engineering

• Use enterprise-approved AI-assisted engineering and testing tools to accelerate test design, automation development, documentation, and analysis.
• Apply AI capabilities, where appropriate, to test scenario generation, test case creation, automation code assistance, test data generation, code/test review, defect analysis, log analysis, root-cause investigation, and requirements-to-test traceability.
• Review and validate AI-generated test artifacts before incorporating them into production QA processes.
• Ensure AI-assisted testing follows applicable enterprise security, privacy, compliance, intellectual-property, and responsible-AI requirements.
• Experience with GitHub Copilot or comparable enterprise-approved AI coding/testing assistants is preferred; no single AI tool is mandatory.

CI/CD & Continuous Testing

• Integrate automated test suites into CI/CD pipelines and enable appropriate testing for pull requests, builds, deployments, and releases.
• Work with DevOps and engineering teams to establish automated quality gates and fast feedback loops.
• Ensure test failures, logs, reports, and diagnostic information are actionable for engineering teams.
• Promote shift-left and continuous-testing practices without sacrificing appropriate end-to-end validation.

Test Data, Environments & Quality Metrics

• Develop reliable and repeatable test-data strategies, including data-driven and synthetic-data approaches where appropriate.
• Coordinate test environment and dependency requirements across development, integration, and vendor teams.
• Protect sensitive information when creating, using, or managing test data.
• Lead defect triage and root-cause analysis for recurring product and automation failures.
• Track and communicate meaningful quality indicators such as automation coverage, pass/failure trends, flaky-test rate, regression execution time, defect leakage, defect trends, and release readiness.

Cross-Functional Collaboration

• Collaborate with Solution Architects, Developers, Product Owners, Business Analysts, Scrum Masters, DevOps Engineers, QA Engineers, and vendor/offshore teams.
• Review requirements and acceptance criteria for clarity and testability.
• Participate in sprint planning, backlog refinement, architecture reviews, release planning, and production-readiness discussions.
• Serve as the primary onshore QA technical contact for assigned initiatives and communicate quality risks clearly to technical and business stakeholders.

Required Qualifications

Experience

• 8+ years of overall IT / Quality Engineering experience.
• 5+ years of hands-on test automation experience.
• Demonstrated experience as a QA Lead, Test Automation Lead, SDET Lead, or Senior Quality Engineer.
• Experience leading QA activities across distributed onshore/offshore teams.
• Strong experience testing enterprise applications, APIs, integrations, and microservices.

Core Automation Engineering Competencies

• Strong understanding of test automation architecture, framework design, testability, maintainability, reliability, and appropriate test-layer selection.
• Ability to evaluate and select automation technologies based on technical and business requirements.
• Strong programming or scripting ability sufficient to build, review, troubleshoot, and maintain production-quality test automation.
• Experience with API testing, integration testing, regression automation, test-data management, mocking/stubbing, and automated reporting.
• Understanding of CI/CD, version control, code review, debugging, logging, and modern software-delivery practices.

Representative Automation Technologies - Examples, Not Mandatory

• UI: Selenium, Playwright, Cypress, or equivalent.
• API / Service: REST Assured, Postman/Newman, or equivalent.
• Java testing: JUnit, TestNG, Cucumber/BDD, or equivalent.
• Python testing: PyTest or equivalent.
• Performance: JMeter, Gatling, k6, or equivalent where applicable.

Application Technology Experience

• Experience testing applications developed with Java, Spring Boot, Node.js, Python, REST APIs, and/or microservices architectures.
• The candidate does not need to be an application developer, but should be technical enough to understand application architecture, APIs, logs, data flows, dependencies, failure modes, and integration patterns.

CI/CD & Engineering Tooling

• Experience with Git and modern source-control workflows.
• Experience with CI/CD platforms such as Jenkins, GitHub Actions, Azure DevOps, GitLab CI, or comparable platforms.
• Familiarity with build/package ecosystems such as Maven, Gradle, npm, and Python package tooling.
• Experience with Docker and containerized application testing is preferred.

AI Skills

• Practical experience using Generative AI or AI-assisted engineering tools within software development or testing workflows.
• Ability to use AI responsibly for test generation, automation assistance, log/defect analysis, test-data creation, documentation, and test-coverage improvement.
• Understanding that AI-generated test cases, code, and analysis require human validation and engineering review before adoption.

Preferred Qualifications

• Experience in healthcare or health insurance / payer environments.
• Experience testing high-volume enterprise applications and microservices.
• Experience with cloud-hosted applications on AWS, Azure, or Google Cloud Platform.
• Experience with Docker and/or Kubernetes.
• Experience with SQL and backend data validation.
• Familiarity with security, accessibility, service virtualization, and performance testing.
• Strong understanding of Agile/Scrum delivery and production-readiness practices.

Leadership & Communication Skills

• Strong verbal and written communication skills.
• Ability to communicate quality risks, tradeoffs, and recommendations to technical and business stakeholders.
• Ability to lead QA strategy sessions, technical reviews, and automation design discussions.
• Strong analytical, troubleshooting, and root-cause-analysis skills.
• Ability to mentor engineers and drive adoption of sustainable automation practices across teams.

Candidate Profile

The ideal candidate is not simply a manual QA lead who has used automation tools. We are looking for a hands-on Quality Engineering / Automation Lead who understands automation as an engineering discipline and can establish the strategy, architecture, standards, and implementation approach for a modern enterprise testing practice.
The candidate should be comfortable operating across:
Requirements → Test Strategy → Automation Architecture → API / UI / Integration Testing → CI/CD → Quality Gates → Defect Analysis → Production Readiness
Above all, the role emphasizes automation engineering fundamentals rather than mastery of a particular tool. A strong candidate should be able to learn or adapt to different testing technologies while consistently applying sound architecture, coding, quality, and governance principles.