Lead Engineer in Test

Job Summary

Required Qualifications

• •Lead QA automation activities across a range of digital initiatives
• •Define and evolve QA strategy, frameworks, and tooling
• •Oversee testing of web applications, APIs, and integrated platforms
• •Incorporate AI-driven testing approaches to improve coverage, speed, and defect detection
• •Build and grow automation capability while mentoring engineers across geographically distributed QA teams
• •Regularly interface with senior leadership and represent automation goals within the context of broader organizational objectives
• •Provide guidance to automation team members on adopting new tools, improving reliability, and scaling automation coverage
• •Lead the definition, execution, and maintenance of standardized automation QA processes, methodologies, and reporting
• •Design and implement robust automation frameworks using modern tools and best practices
• •Drive QA process improvements using industry best practices and emerging technologies
• •Stay current on digital technologies, testing trends, and AI-assisted QA techniques to support innovation and client demands
• •Represent the QA capability in cross-organizational meetings and client-facing discussions
• •Hire, train, and mentor QA engineers and automation resources
• •Lead and facilitate automation development across geographically distributed QA teams in fast-paced, agile environments
• •Design, implement, and maintain scalable test automation frameworks from scratch using Python or Node.js
• •Demonstrate understanding of Design Patterns, OOP, and modular code structure
• •Advanced implementation of Microsoft Playwright for Robust Cross-browser UI automation (handling flakes, dynamic waits, and authentication states)
• •Utilizing Playwright's APIRequestContext or libraries like requests/supertest to build integrated end-to-end workflows (UI + API)
• •Designing tests that bypass UI for setup/teardown using direct API calls (optimizing execution time)
• •Experience mocking/stubbing network requests to isolate frontend components and test edge cases
• •Ownership of the automation pipeline. Integrating tests into GitLab CI/Azure DevOps/Jenkins, managing Docker containers for execution, and setting up quality gates
• •Define the testing strategy (Test Pyramid), deciding when to use API vs. UI testing to maximize ROI and stability
• •Experience with Selenium or Cypress to understand migration paths and architectural differences
• •Proficient in Python or Node.js with a strong adherence to SOLID principles and Design Patterns (Page Object Model, Factory, Singleton, Strategy) to build maintainable and scalable automation frameworks
• •Solid understanding of HTML, DOM manipulation, and CSS/XPath selectors
• •Familiarity with modern JS frameworks (React, Angular, Vue) to understand component lifecycles, shadow DOM, and optimize locator strategies (moving beyond fragile selectors)
• •Ability to write clean, reusable, and self-documenting code
• •Experience conducting Code Reviews and enforcing coding standards within the QA team
• •Deep understanding of multi-threading, multiprocessing, and asynchronous programming patterns
• •Mastery of the Event Loop, non-blocking I/O, Promise.all patterns, and Worker Threads for CPU-intensive tasks (for Node.js)
• •Experience managing the GIL (Global Interpreter Lock), utilizing asyncio for I/O bound tasks, and multiprocessing for parallel data processing (for Python)
• •Experience integrating LLM APIs (OpenAI, Anthropic, Gemini) directly into automation frameworks for tasks like automated root cause analysis of failure logs or 'self-healing' mechanisms for broken UI selectors
• •Leveraging Generative AI to programmatically create large-scale, diverse, and anonymized datasets (PII-free) to cover complex edge cases and boundary conditions
• •Strategies for validating probabilistic outputs, including semantic similarity checks (using embeddings/vector distance) rather than exact string matching
• •Validating guardrails against hallucinations, prompt injection attacks, and bias, ensuring the AI behaves within defined safety boundaries
• •Understanding of responsible AI principles, governance, and risk management in QA contexts
• •Ability to evaluate 'what to automate' vs. 'what to test manually,' prioritizing flows based on business risk, technical complexity, and maintenance cost
• •Define and track KPIs such as Flakiness Rate, Cycle Time, and Defect Escape Rate to measure the health of the automation suite and drive continuous improvement
• •Champion 'Quality Engineering' principles early in the SDLC, participating in architecture reviews and design discussions to ensure features are testable before code is written
• •Collaborate effectively with DevOps, Product, and Backend teams to remove blockers, improve test data availability, and align automation goals with business delivery timelines
• •Lead the strategy for complex, large-scale platforms, orchestrating not just functional automation but integrating performance, accessibility, and security checks into the continuous delivery pipeline
• •Proven ability to translate ambiguous business requirements into scalable, automated technical solutions that address root operational inefficiencies, going beyond traditional QA scope

Desired Qualifications

• •Accessibility testing (WCAG / Section 508)
• •Performance testing tools (JMeter, k6, Locust)
• •Cloud-based test execution and environment orchestration
• •Experience testing AI-driven or data-heavy platforms