Director, Quality Assurance

Hearst•Tampa, FL

1d•Hybrid

About The Position

The Director, Quality Assurance is a hybrid-based opportunity in Tampa that will lead and scale a modern quality engineering and automation organization. This role is responsible for defining and executing an enterprise-wide quality strategy, including automation frameworks, BDD/TDD practices, and AI validation standards across the full Agile development lifecycle. This team leader will drive the transformation from traditional QA to quality engineering, embedding quality early and continuously while enabling fast, reliable, and responsible product delivery. The Director will partner closely with Engineering, Product, Architecture, Data/AI, Security, and DevOps to ensure predictable, high-quality releases that meet customer, regulatory, and ethical expectations in a healthcare SaaS environment.

Requirements

7–10+ years of experience in software testing, QA, or quality engineering
7+ years leading QA or quality engineering teams, including globally distributed teams
5+ years of hands-on automation experience, including building frameworks from scratch
Strong expertise in: BDD/TDD methodologies Agile development practices Test automation frameworks (e.g., Selenium, Cucumber)
Experience with: API testing and integration testing CI/CD tools (e.g., Jenkins) Relational databases (e.g., MySQL) and web technologies (HTML/CSS/JavaScript)
Proven ability to drive quality strategy and engineering transformation
Strong leadership, responsiveness, and analytical skills that foster a lasting confidence in our solutions and services.

Nice To Haves

Experience validating AI/ML or data-driven systems preferred.
Healthcare or regulated SaaS experience (HIPAA, CMS, etc.) preferred.
Experience with distributed systems and enterprise platforms preferred.
Familiarity with tools such as Selenium Grid, Jira, Prometheus, Grafana.
Experience supporting cloud-based environments (AWS preferred).
Ability to work across all time zones as necessary.
Employment is contingent on successfully completing a background check and drug screening.

Responsibilities

Build, lead, and scale high-performing QA and Quality Engineering teams, including hiring, mentoring, coaching, and performance management.
Establishes governance and cadence that fostesr trust, quality ownership, accountability, and continuous improvement.
Develop capabilities across automation, data quality, and AI validation.
Manage and grow globally distributed teams and external vendors.
Build and grow client trust organically as a product advisor by proving an ongoing value through accountability, proactive communication and transparency, while providing tailored solutions aligned to each client’s goals and use cases
Define and own the enterprise QA, testing, and quality engineering strategy.
Establish and enforce BDD/TDD practices, shift-left testing, and quality gates.
Develop standards for both deterministic systems and AI-driven/probabilistic systems.
Ensure release readiness and production quality standards are consistently met.
Maintain a high level of professionalism, accountability, and responsiveness in all client interactions.
Define and implement AI validation frameworks, including: Accuracy and reliability Bias detection and mitigation Explainability and transparency
Support data quality, monitoring, and drift detection practices.
Ensure compliance with regulatory, governance, and ethical AI standards.
Lead QA efforts across multiple Agile/Scrum teams.
Participate in PI planning, sprint ceremonies, and backlog refinement.
Partner cross-functionally to define acceptance criteria and release sign-off.
Support continuous delivery and production stability.
Define and execute the enterprise test automation strategy across: UI / Web applications APIs and integrations Performance and load testing Security testing AI and data validation
Oversee development of scalable automation frameworks (e.g., Java, Selenium, BDD/Cucumber).
Integrate automation into CI/CD pipelines (e.g., Jenkins or similar).
Standardize QA and AI testing tools and practices across teams.
Define and track quality metrics, including defect trends, release quality, and automation coverage.
Drive root-cause analysis (RCA) and continuous improvement initiatives.
Reduce production defects and improve system reliability through data-driven insights.