Lead Software Test Automation Engineer

Wolters Kluwer•Coppell, TX

1d•$89,600 - $157,000

About The Position

The Lead Software Test Automation Engineer defines and leads an enterprise-grade test automation strategy across UI, API, data, integration, and platform layers, while advancing quality engineering for AI and agentic systems. The role designs and scales automation frameworks, establishes measurable quality gates for both deterministic and non-deterministic product behavior, and builds reliable, maintainable automation that accelerates delivery. It also defines validation approaches for probabilistic AI outcomes, including evaluation datasets, threshold- and rubric-based validation, groundedness, regression detection, robustness, performance, and reliability, and embeds automation into CI/CD and production-quality practices. The role serves as the technical authority for software test automation, establishing engineering standards, leading design and code reviews, coaching engineers, and partnering across Product, Software Engineering, AI Engineering, Security, DevOps, and Operations. It uses product telemetry, incidents, customer issues, and defect escape patterns to improve coverage and prevent recurrence. A bachelor's degree or equivalent practical experience is required; a relevant master's degree is preferred.

Requirements

8+ years of software quality engineering or test automation experience, including significant ownership of automation frameworks, technical design, and strategy for complex commercial software.
Hands-on experience designing and building scalable automation across API, contract, integration, UI, data, and end-to-end layers.
Strong programming skills in at least one modern language such as Java, Python, TypeScript/JavaScript, or C#.
Hands-on experience with modern UI and API automation frameworks such as Playwright, Cypress, Selenium, REST Assured, SuperTest, Postman/Newman, or pytest.
Experience integrating automated testing into CI/CD using platforms such as GitHub Actions, Jenkins, or Azure DevOps, including automated quality gates and release-readiness criteria.
Demonstrated ability to improve test reliability and execution speed through engineering solutions, including reducing flakiness, parallelization, test selection, and effective diagnostics.
Experience defining test data strategies, managing test environments, service virtualization, and dependency control for distributed systems.
Hands-on experience validating AI/ML, generative AI, agentic, or other probabilistic product capabilities in production-oriented software, including evaluation harnesses, benchmark datasets, semantic or rubric-based scoring, groundedness, and regression evaluation.
Practical experience with Agentic Product Development Lifecycle and Spec-Driven Development, including translating specifications and acceptance criteria into traceable automated validation.
Working knowledge of security, performance, accessibility, resilience, and reliability testing within modern software delivery practices.
Strong test automation design and systems-thinking skills across application, API, integration, data, platform, and AI/agentic boundaries.
Deep understanding of modern quality engineering practices, including the test pyramid, shift-left validation, contract testing, risk-based testing, and appropriate use of end-to-end automation.
Strong understanding of testing probabilistic AI and agentic systems, including evaluation design, benchmark datasets, thresholds, semantic and rubric-based scoring, groundedness, robustness, regression, and traceability.
Ability to design automation for speed, determinism, maintainability, diagnosability, and reuse while avoiding brittle or over-engineered frameworks.
Strong understanding of CI/CD, automated quality gates, test observability, telemetry, environment strategy, and production feedback loops.
Ability to define meaningful quality measures and dashboards that distinguish product risk and release confidence from simple test counts or pass rates.
Strong coding, code-review, debugging, and root-cause analysis skills with an engineering-first approach to test automation.
Ability to lead through technical influence, establish automation standards, coach engineers, and drive quality ownership across Product, Software Engineering, AI Engineering, DevOps, and Operations.
Strong communication skills and the ability to translate technical and product risk into clear testing priorities, quality decisions, and pragmatic trade-offs.
Ability to balance delivery speed, automation coverage, reliability, maintainability, and business risk without positioning Quality Engineering as a downstream gate.
Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.

Nice To Haves

Master's degree in a relevant technical or business discipline.

Responsibilities

Define and own the test automation technical design and risk-based, multi-layer strategy across UI, API, contract, component, integration, end-to-end, data, and platform testing.
Establish automation standards, patterns, and reference implementations for framework structure, coding practices, reusable components, test data, diagnostics, reporting, and maintainability.
Drive shift-left quality practices through component, contract, API, and pre-merge validation, reducing unnecessary reliance on slow or fragile end-to-end tests.
Design validation for non-deterministic AI and agentic outcomes, including confidence- and threshold-based assertions, semantic and rubric-based evaluation, benchmark datasets, evaluation harnesses, groundedness testing, and regression detection across model, prompt, context, and agent changes.
Define AI quality coverage and measurable acceptance criteria for accuracy, robustness, groundedness, safety where applicable, performance, reliability, and cost-aware execution with AI Engineering, Product, and Software Engineering.
Build and scale high-throughput automation suites that are reliable, fast, diagnosable, and easy to extend across products and delivery teams.
Improve automation execution time and stability through parallelization, intelligent test selection, dependency control, deterministic test data, and disciplined retry strategies.
Integrate automated validation into CI/CD through pull-request checks, scheduled suites, deployment gates, and release-readiness criteria.
Standardize test observability, including logs, traces, screenshots or video where appropriate, artifacts, failure diagnostics, and quality dashboards covering execution health, flakiness, coverage, and escaped defects.
Define effective practices for test environments, test data management, service virtualization, external dependency control, and repeatable environment setup.
Lead design and code reviews, coach engineers on automation design and code quality, and establish maintainable engineering standards.
Influence product and engineering roadmaps by translating customer, technical, operational, and delivery risks into validation priorities and quality investments.
Partner with Product, Software Engineering, AI Engineering, Security, DevOps, and Operations to engineer functional and nonfunctional quality throughout the product lifecycle.
Use production telemetry, incidents, customer issues, and defect escape patterns to improve automated coverage and prevent recurrence.