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Wolters Kluwer

Offers professional information, software, and services

Lead Software Test Automation Engineer

Test Automation EngineeringUpdated on Nov 6
Full-timeCoppell, TX, USA

About the Job

Requirements

  • A Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
  • At least 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 for automation 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 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.

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 covering 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 and implement validation approaches 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 across accuracy, robustness, groundedness, safety where applicable, performance, reliability, and cost-aware execution in partnership 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 with appropriate 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 actionable 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.
  • Serve as the technical authority for software test automation; 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 clear validation priorities and quality investments.
  • Partner with Product, Software Engineering, AI Engineering, Security, DevOps, and Operations to ensure functional and nonfunctional quality is engineered throughout the product lifecycle.
  • Use production telemetry, incidents, customer issues, and defect escape patterns to continuously improve automated coverage and prevent recurrence.

Desired Qualifications

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

About the Company

Wolters Kluwer

Offers professional information, software, and services

Wolters Kluwer is a global provider of professional information, software solutions, and services for professionals in legal, business, tax, accounting, finance, audit, risk, compliance, and healthcare. Its products are offered on a subscription basis and include software tools and access to professional information, plus consulting and one-time reports. The software and information help clients improve financial performance, comply with regulations, and manage ESG and other risk areas; ESG solutions support sustainability goals and risk management. The company integrates software, content, and expert guidance rather than relying on a single product, and it operates in a specialized market with a strong emphasis on regulatory compliance and ethical practices. Wolters Kluwer’s goal is to help customers perform better, stay compliant with evolving rules, and manage risk in a responsible, transparent way.

Company size
10,001+
Company stage
IPO
Headquarters
Alphen aan den Rijn, Netherlands
Founded
1835

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