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Director Quality Engineering at Baylor Scott & White Health, Dallas, TX

Director Quality Engineering at Baylor Scott & White Health in Dallas, TX. Est. up to $200k. Apply with swipeandhired.

The Director of Quality Engineering leads the transformation of BSWH’s quality assurance approach from primarily manual testing to a modern, automation-first quality engineering model. This role is accountable for establishing enterprise quality engineering practices that improve both product quality and speed of delivery across BSWH technology teams.
This leader owns quality engineering strategy, test automation, AI-enabled testing practices, quality metrics, release readiness, and the operating model for QA resources across BSWH. The role partners closely with Engineering, Product, Architecture, AI Foundation, Data & Analytics, Security, Privacy, Clinical, Operations, and vendor/partner teams to ensure that software and AI-enabled products are delivered safely, reliably, and efficiently.
The Director of Quality Engineering is responsible for building scalable quality practices that support modern engineering delivery, including shift-left testing, automated regression, API and UI automation, quality gates, performance testing, production validation, AI evaluation support, and continuous improvement. This role is a critical enabler of delivery velocity, product reliability, and customer trust.
This position can be based in our administrative building in Dallas, Texas or mostly remote with some travel required.
ESSENTIAL FUNCTIONS OF THE ROLE
Quality Engineering Strategy & Transformation

Define and lead the enterprise quality engineering strategy for BSWH, shifting the organization from traditional manual QA toward automation-first, engineering-integrated, and AI-enabled quality practices.
Establish a multi-year roadmap for quality modernization, including test automation, tooling, metrics, delivery integration, talent development, and operating model changes.
Set enterprise standards for quality engineering across digital products, application teams, platform teams, AI use cases, and shared technology services.
Create clear expectations for when testing should be automated, manually validated, embedded within engineering teams, or governed through centralized quality standards.
Drive adoption of quality engineering practices that improve release confidence while reducing cycle time, rework, and dependency on late-stage manual testing.

Test Automation & AI-Enabled Testing

Lead the design and implementation of scalable test automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing.
Introduce AI-enabled testing capabilities where appropriate, including test generation, test maintenance, defect analysis, intelligent regression selection, synthetic data support, and productivity acceleration for QA teams.
Establish automation coverage targets, automation quality standards, and reporting mechanisms that make test effectiveness visible to engineering and product leadership.
Partner with engineering teams to embed automated testing into CI/CD pipelines, release gates, and development workflows.
Continuously evaluate tooling, frameworks, and emerging AI capabilities that can improve quality, reliability, and delivery speed.

AI Quality & Evaluation Partnership

Partner with AI Architecture, AI Foundation, Engineering, Product, and Data teams to define quality practices for AI-enabled and agentic systems.
Support the development of evaluation approaches for AI-enabled workflows, including expected behavior, acceptance criteria, guardrail validation, regression testing, hallucination/error detection, escalation patterns, and human-in-the-loop validation.
Ensure AI-enabled products have appropriate quality measures for accuracy, consistency, safety, traceability, source attribution, fallback behavior, and operational readiness.
Work with engineering and AI quality stakeholders to integrate test automation, evaluation frameworks, monitoring, and feedback loops into AI delivery practices.
Help ensure AI use cases can scale safely without relying on one-off or purely manual validation approaches.

Release Quality, Reliability & Operational Readiness

Define release-readiness standards, quality gates, defect triage processes, regression expectations, test evidence requirements, and production validation practices.
Ensure teams have clear quality metrics and release criteria for customer-facing and enterprise technology products.
Partner with Engineering, DevSecOps, Operations, and Security teams to embed quality into CI/CD, deployment, monitoring, rollback, and incident response workflows.
Establish practices for performance testing, reliability testing, accessibility testing, security testing coordination, and supportability validation.
Drive continuous improvement in quality outcomes, defect leakage, release predictability, test cycle time, automation coverage, and production stability.

Enterprise QA Operating Model & Talent Leadership

Oversee QA resources across BSWH and establish a consistent operating model for quality engineering roles, responsibilities, standards, and engagement with product and engineering teams.
Build, coach, and develop QA and quality engineering talent with an emphasis on automation, engineering partnership, AI-enabled productivity, and continuous learning.
Clarify the distinction between manual testing, quality engineering, test automation, product acceptance, engineering-owned quality, and AI evaluation responsibilities.
Partner with leaders across technology teams to determine resource models, capability gaps, upskilling needs, partner support, and hiring requirements.
Create a quality engineering culture focused on speed, accountability, automation, proactive risk management, and measurable customer impact.

Cross-Functional Partnership & Governance

Partner with Product, Engineering, Architecture, Security, Privacy, Compliance, Clinical, Data, Operations, and vendor teams to ensure quality expectations are defined early and embedded throughout delivery.
Establish governance forums, metrics reviews, standards, and playbooks that make quality expectations clear and actionable across teams.
Ensure quality practices align with enterprise technology standards, responsible AI expectations, data protection requirements, and regulated-industry obligations.
Communicate quality strategy, modernization progress, delivery risks, tooling decisions, and performance metrics to leadership and stakeholders.
Serve as the senior quality engineering advisor for major technology and AI-enabled product initiatives.

KEY SUCCESS FACTORS

Proven ability to lead quality engineering transformation from manual testing-heavy models to automation-first, engineering-integrated quality practices.
Deep expertise in test automation strategy, tooling, framework design, CI/CD integration, quality metrics, release gates, and scalable QA operating models.
Strong understanding of modern software delivery practices, including agile delivery, DevSecOps, shift-left testing, automated regression, API testing, UI testing, performance testing, and production validation.
Ability to apply AI and automation to improve QA productivity, expand test coverage, reduce cycle time, and increase release confidence.
Strong understanding of quality challenges for AI-enabled products, including testing expected behavior, evaluation criteria, guardrails, traceability, safety, and regression risk.
Demonstrated ability to influence engineering, product, architecture, operations, security, and business stakeholders around common quality standards and delivery objectives.
Strong talent leadership, including building, coaching, upskilling, and operating quality engineering teams across a complex enterprise environment.
Ability to balance speed of delivery with reliability, compliance, customer trust, operational readiness, and long-term maintainability.
Comfort operating in regulated, high-trust, or mission-critical environments where quality, safety, privacy, auditability, and reliability are essential.
Strong communication and change-management skills, with the ability to shift organizational habits and expectations around quality ownership.

PREFERRED QUALIFICATIONS
Education

Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related technical field.
Master’s degree preferred.

Experience

15–18+ years of technology, software quality, quality engineering, test automation, software engineering, or related technology delivery experience.
7–10+ years of QA, quality engineering, automation, or engineering leadership experience.
Demonstrated experience modernizing QA practices, scaling test automation, or transforming manual testing models into automation-first quality engineering capabilities.
Experience leading multidisciplinary quality teams that may include QA analysts, test automation engineers, quality engineers, SDETs, performance testers, and partner/vendor QA resources.
Experience embedding quality practices into modern engineering workflows, including agile delivery, CI/CD, DevSecOps, automated testing, release readiness, monitoring, and production validation.
Experience partnering with product, engineering, architecture, security, privacy, operations, and business stakeholders to deliver complex technology-enabled outcomes.
Experience with AI-enabled, data-intensive, customer-facing, or mission-critical product delivery preferred.
Experience working in healthcare, life sciences, financial services, or another regulated/high-trust environment preferred.
Experience leading enterprise quality engineering, test automation, SDET, or QA transformation programs.
Experience building automation-first quality models across multiple product teams, platforms, or engineering portfolios.
Experience with AI, GenAI, machine learning, conversational AI, automation, workflow systems, or decision-support products.
Experience establishing quality engineering standards, tooling strategies, test automation roadmaps, release-readiness playbooks, and measurable quality metrics.
Experience using AI or automation tools to improve test creation, maintenance, analysis, coverage, regression selection, or QA productivity.
Experience with regulated-industry technology delivery, including healthcare, life sciences, financial services, or other environments with sensitive data and auditability requirements.
Experience working with external partners, vendors, systems integrators, or distributed engineering teams while maintaining internal quality ownership and standards.
Experience leading through organizational change, technical ambiguity, emerging technology, and evolving delivery practices.

Required Technical Expertise

Strong technical foundation in software quality engineering, test automation, application delivery, API testing, integration testing, UI testing, and release validation.
Experience with modern automation frameworks, test management practices, CI/CD integration, quality dashboards, defect analytics, and release-readiness reporting.
Understanding of AI-enabled testing approaches, including test generation, intelligent test selection, AI-assisted defect analysis, synthetic data support, and automation productivity tools.
Familiarity with quality practices for AI-enabled systems, including evaluation frameworks, expected behavior definition, guardrail validation, traceability, monitoring, and regression testing.
Ability to work effectively with engineers, architects, platform teams, product managers, data teams, and operations teams to embed quality into delivery workflows.
Strong understanding of security, privacy, compliance, reliability, accessibility, and customer trust considerations for enterprise technology systems handling sensitive data.

MINIMUM REQUIREMENTS

Bachelor’s Degree or 4 years of experience above minimum qualifications
7 years of experience