Lead Quality Assurance Engineer in Test

Lead QA Engineer in Test

Our advanced Technology capability delivers cutting-edge, high-impact digital products and campaigns. We bring strategic partnerships with the world’s leading marketing and technology vendors that allow us to represent the top platforms and tools for our clients. Through collaboration with Strategy, Experience Design, Data Science, Project Management, and Account teams, our technologists are helping the world’s leading brands transform the marketing landscape. Our Quality Assurance (QA) team keeps us technologically forward and on point. 

To help with this, we are looking for a Lead QA Engineer in Test — someone who can lead our QA automation team forward, establishing structure and best-in-class growth. This role spans the full QA lifecycle and works closely with cross-functional teams to modernize efficiencies, implement best-in-class QA automation methodologies and tools, and drive quality at scale across complex digital ecosystems. 

The successful candidate is a hands-on QA leader with deep experience in automation development, strong programming skills, and a forward-looking mindset that includes AI-assisted testing, intelligent automation, and modern test orchestration frameworks. 

Role Overview 

From test planning and execution to technical risk management and process improvement, the Lead QA Engineer in Test supports complex digital projects that are defined, designed, and developed by Digitas and other Publicis agencies. 

As a Lead QA Engineer in Test, you will: 

• 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 

Key Responsibilities 

• 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 

Technical & Automation Expertise 

Automation & Programming 

• Framework Architecture: Design, implement, and maintain scalable test automation frameworks from scratch using Python or Node.js. (Must demonstrate understanding of Design Patterns, OOP, and modular code structure). 

• Playwright Expertise: Advanced implementation of Microsoft Playwright for: 

• Robust Cross-browser UI automation (handling flakes, dynamic waits, and authentication states). 

• Programmatic API Testing: Utilizing Playwright’s APIRequestContext or libraries like requests/supertest to build integrated end-to-end workflows (UI + API). 

• API & Hybrid Testing: Designing tests that bypass UI for setup/teardown using direct API calls (optimizing execution time). 

• Network Interception: Experience mocking/stubbing network requests to isolate frontend components and test edge cases. 

• CI/CD & DevOps Integration: Ownership of the automation pipeline. Integrating tests into GitLab CI/Azure DevOps/Jenkins, managing Docker containers for execution, and setting up quality gates. 

• Strategy & Leadership: Define the testing strategy (Test Pyramid), deciding when to use API vs. UI testing to maximize ROI and stability. 

• Legacy/Migration Knowledge: Experience with Selenium or Cypress to understand migration paths and architectural differences. 

• Software Design Excellence: 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. 

• Modern Frontend Awareness: 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). 

• Code Quality Standards: Ability to write clean, reusable, and self-documenting code. Experience conducting Code Reviews and enforcing coding standards within the QA team. 

• Concurrency & Parallel Execution: Deep understanding of multi-threading, multiprocessing, and asynchronous programming patterns. 

• For Node.js: Mastery of the Event Loop, non-blocking I/O, Promise.all patterns, and Worker Threads for CPU-intensive tasks. 

• For Python: Experience managing the GIL (Global Interpreter Lock), utilizing asyncio for I/O bound tasks, and multiprocessing for parallel data processing. 

AI & Intelligent Automation 

• AI Integration: 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. 

• Synthetic Data Engineering: Leveraging Generative AI to programmatically create large-scale, diverse, and anonymized datasets (PII-free) to cover complex edge cases and boundary conditions. 

• Testing Non-Deterministic Systems: Strategies for validating probabilistic outputs, including semantic similarity checks (using embeddings/vector distance) rather than exact string matching. 

• Risk & Compliance: 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 

QA Strategy & Leadership 

• Automation ROI Analysis: Ability to evaluate "what to automate" vs. "what to test manually," prioritizing flows based on business risk, technical complexity, and maintenance cost (avoiding the "automating for the sake of automation" trap). 

• Metric-Driven Improvements: 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. 

• Shift-Left Advocacy: Champion "Quality Engineering" principles early in the SDLC, participating in architecture reviews and design discussions to ensure features are testable before code is written. 

• Cross-Functional Influence: Collaborate effectively with DevOps, Product, and Backend teams to remove blockers, improve test data availability, and align automation goals with business delivery timelines. 

• Holistic Quality Architecture: 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. 

• Solution Engineering: Proven ability to translate ambiguous business requirements into scalable, automated technical solutions that address root operational inefficiencies, going beyond traditional QA scope. 

Nice-to-Have Experience 

• 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