We are looking for a Senior Automation Architect to lead our test automation strategy across UI, API, and data testing. You will design scalable automation frameworks, drive tooling decisions, integrate quality into CI/CD pipelines, lead data lake and ETL validation efforts, and mentor engineering teams. This is a hands-on leadership role requiring strong technical depth in both application and data testing, with the ability to deliver quality at speed.

Key Responsibilities :

- Architect and maintain automation frameworks using Selenium WebDriver (Java), Playwright, and REST Assured, applying design patterns like Page Object Model and BDD.

- Design and implement API test suites covering functional, contract, schema, and negative testing using REST Assured and Postman/Newman.

- Build and optimize CI/CD test pipelines in GitHub Actions (workflows, reusable actions, matrix strategies), Jenkins, and AWS pipelines (CodePipeline, CodeBuild) with parallel execution, quality gates, and containerized test environments (Docker/K8s).

- Leverage AWS infrastructure (EC2, ECS, S3, Lambda, CodePipeline, CloudWatch) for provisioning scalable test environments, storing test artifacts, and monitoring pipeline health.

- Possess strong knowledge of performance testing tools and methodologies including JMeter (distributed mode, custom plugins, correlation), Gatling, or k6 with the ability to support load, stress, soak, spike, and scalability testing efforts through throughput/latency analysis and bottleneck identification.

- Design data validation frameworks for SQL/NoSQL databases, including SQL script optimization (query tuning, execution plan analysis, indexing strategies) and ETL/data pipeline validation for data migration and warehousing verification.

- Lead AWS Data Lake testing across S3, Glue, Athena, and Redshift validating data ingestion, transformation, storage, and query accuracy across the data pipeline.

- Define and implement ODS (Operational Data Store) architecture validation strategies, ensuring data consistency, completeness, and integrity across source-to-target flows.

- Own and maintain the enterprise test automation strategy and roadmap, including framework selection rationale, coverage goals, tooling decisions, and long-term evolution plans ensuring alignment with product and engineering leadership.

- Define automation standards, conduct code reviews, and mentor QA engineers and SDETs across teams.

- Evaluate emerging tools and AI-driven testing approaches; leverage GitHub Copilot for accelerated test code generation, code reviews, and productivity improvements across automation teams.

Technical Skills Matrix :

- UI Automation : Selenium WebDriver (Java), Playwright (TypeScript/Java), TestNG, JUnit 5, Cucumber BDD

- API Testing : REST Assured, Postman/Newman, Contract Testing (Pact)

- Programming : Core Java (advanced), TypeScript/JavaScript, Python

- CI/CD & DevOps : GitHub Actions (workflows, reusable actions, matrix builds), Jenkins, GitLab CI/CD, Docker, Kubernetes

- Build & Version Control : Maven, Gradle, Git (advanced), GitHub/GitLab branching strategies

- Cloud & Infrastructure : AWS (EC2, ECS, S3, Lambda, CodePipeline, CloudWatch, Device Farm), Selenium Grid 4, Playwright sharding, BrowserStack, Sauce Labs, cross-browser and cross-device parallel execution

- Database & SQL : SQL (complex queries, stored procedures, query optimization, execution plans, indexing strategies), MongoDB, ETL/data pipeline validation, data warehousing concepts, ODS architecture

- AWS Data & Analytics : S3, Glue, Athena, Redshift validation, Data Lake testing, CodePipeline

Qualifications :

- Bachelor s or Master s degree in Computer Science, Engineering, or a related discipline.

- 16+ years of hands-on QA and test automation experience with a strong focus on data testing and automation, and at least 5-6 years in an architect or lead role.

- Strong expertise in SQL complex queries, stored procedures, query optimization, execution plan analysis, indexing strategies, and stored procedure performance for database-heavy applications.

- Advanced hands-on experience in Java programming with deep expertise in Selenium WebDriver and strong proficiency in Playwright for modern browser automation.

- Strong experience in automation frameworks: TestNG, BDD (Cucumber), with proven experience in designing and maintaining scalable automation frameworks using design patterns (POM, Factory, Strategy).

- Mandatory hands-on experience in AWS Data Lake testing (S3, Glue, Athena, Redshift validation) and strong experience in ETL/data pipeline validation and data warehousing concepts.

- Strong understanding of ODS architecture and data validation strategies, including source-to-target reconciliation and data integrity checks.

- Advanced API testing experience with REST Assured including authentication flows (OAuth2, JWT), response chaining, and schema validation.

- Hands-on CI/CD pipeline engineering with GitHub Actions (workflows, reusable actions, self-hosted runners) and Jenkins, using Docker/Kubernetes-based test environments.

- Strong working knowledge of AWS infrastructure (EC2, ECS, S3, Lambda, CodePipeline, CloudWatch) for test environment provisioning and pipeline orchestration.

- Working knowledge of performance testing tools and methodologies (JMeter, Gatling, k6) with the ability to support load and scalability testing efforts.

- Hands-on experience with GitHub Copilot for AI-assisted test code generation, code reviews, and driving developer productivity across automation teams.

- Strong communication and stakeholder management skills with the ability to influence technical decisions at the leadership level.

Nice to Have :

- Experience with performance testing tools (JMeter distributed testing, Gatling, k6) including load modeling, throughput/latency analysis, and bottleneck identification.

- Experience with mobile automation using Appium (Android/iOS) and cloud device farm integrations (BrowserStack, Sauce Labs).

- Experience building test observability dashboards (Allure, Grafana, Kibana) for tracking pass rates, flakiness, execution trends, and defect density.

- Experience with big data technologies (e.g., Spark, Hadoop).

- Knowledge of data governance and data quality tools.

- Experience with AI/ML-driven testing platforms (Testim, Applitools Visual AI, Mabl).

- ISTQB Advanced certification; AWS/Azure certifications are a plus.

- Contributions to open-source testing frameworks or QA community thought leadership.