Onshore QA Automation Lead Job SummaryWe are seeking an experienced Onshore QA Automation Lead to lead the quality engineering and test automation strategy for enterprise applications and services developed using Java, Spring Boot, Node.js, and Python. The ideal candidate is a hands-on technical leader who can design scalable automation approaches, improve testability, integrate automated testing into delivery pipelines, and guide distributed QA teams.
This role is intentionally tool-agnostic. We value deep understanding of automation architecture, test design, API and integration testing, maintainability, reliability, CI/CD integration, and quality engineering principles over expertise in a particular commercial or open-source testing product. The candidate should also have practical experience using AI-assisted engineering and testing tools to improve QA productivity while maintaining appropriate human review and engineering governance.Key ResponsibilitiesQA 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 QualificationsExperience- 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 MandatoryExperience with one or more of the following, or comparable technologies, is valuable. Candidates are not expected to know every tool:- 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 ProfileThe 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. |
