Senior Software Test Automation Engineer

Wolters Kluwer•Coppell, TX

5d•$71,300 - $124,500

About The Position

Wolters Kluwer is seeking a hands-on Senior Software Test Automation Engineer to own and drive quality strategy for enterprise products. The role carries advanced testing responsibilities and contributes significantly to quality assurance by creating robust test automation solutions that support software reliability and performance. It also includes mentoring junior team members and driving continuous improvement in testing processes. The engineer will define and implement end-to-end quality strategy across UI, API, and non-functional layers; establish automation roadmaps, quality KPIs, and test-pyramid practices; and promote shift-left testing and automation at lower layers. The role also involves release-readiness reviews and risk assessments, influencing engineering teams to adopt quality best practices, and architecting and implementing or modifying scalable automation frameworks using C# (.NET) or advanced Java. The position works with Product, Engineering, and DevOps stakeholders and includes integrating automated tests into CI/CD pipelines, contract testing with OpenAPI/Swagger, mentoring and reviewing automation engineers’ code, and setting technical guidance and framework standards. The engineer will also use AI-assisted development tools and prompting techniques to improve automation productivity, while validating AI-generated outputs for accuracy, maintainability, and security. Candidates should have 5–8+ years in QA with significant automation experience, hands-on framework development, the ability to spend 40–60% of their time coding, and demonstrated leadership in quality initiatives. Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process. Compensation is $71,300–$124,500 USD, with eligibility for a bonus; the actual base salary depends on factors including skills, experience, and hiring location.

Requirements

Strong proficiency in C# (.NET) or advanced Java.
Hands-on experience with Playwright or Selenium.
REST API automation experience using RestSharp or HttpClient.
Strong understanding of JSON, HTTP protocols, and status codes.
Experience with SQL/database validation.
Hands-on knowledge of CI/CD integration using Azure DevOps, GitHub Actions, or Jenkins.
Experience tracking QA KPIs and quality dashboards, such as Grafana.
Agile/Scrum experience.
Working knowledge of performance-testing tools such as JMeter.
Hands-on experience using AI-assisted development tools such as GitHub Copilot or similar.
Ability to craft effective prompts to generate, refactor, and optimize automation code.
Experience validating and refining AI-generated outputs for accuracy, maintainability, and security.
Demonstrated ability to improve engineering productivity using AI tools.
5–8+ years in QA with significant automation experience.
Proven hands-on framework development experience.
Ability to contribute 40–60% in active coding.
Demonstrated leadership in driving quality initiatives.

Responsibilities

Define and implement end-to-end quality strategy across UI, API, and non-functional testing layers.
Establish the automation roadmap and quality KPIs.
Establish and enforce a Test Pyramid model balancing unit, API, integration, and UI automation.
Drive shift-left practices and increase automation coverage at lower testing layers.
Track QA KPIs, including automation coverage, defect leakage, and regression stability.
Drive release-readiness reviews and risk assessments.
Influence engineering teams to adopt quality best practices.
Architect and implement or modify scalable automation frameworks using C# (.NET) or advanced Java.
Integrate automated tests into CI/CD pipelines.
Implement or apply contract testing using OpenAPI/Swagger.
Mentor automation engineers and review their code.
Provide technical guidance and framework standards.
Collaborate with Product, Engineering, and DevOps stakeholders.
Use AI-assisted tools and prompting techniques to improve development and automation productivity.
Validate and refine AI-generated outputs for accuracy, maintainability, and security.