Director of Quality Assurance - Data & Analytics

Director of Quality Assurance – Data & Analytics – Hybrid in Lenexa, KS

This position is based in our Lenexa, KS office. We leverage a hybrid working model working three days in the office, two days optional remote work.

The Director of Quality Assurance Data & Analytics is responsible for leading and evolving the quality assurance function for data and analytics products across the organization. This role provides both strategic leadership and hands‑on QA execution, ensuring the accuracy, reliability, usability, and performance of dashboards, data pipelines, reports, and APIs. The Director partners closely with cross‑functional teams to establish scalable QA practices, drive consistent testing operations across multiple teams, and build a strong culture of quality.

This position also directly contributes to test strategy, test execution, defect triage, and process improvement for complex data and analytics solutions.

• Lead the QA function for data and analytics products, defining direction, priorities, and measures of success.
• Build, scale, and manage, and develop a QA program that supports multiple teams, products, and delivery streams.
• Drive cross‑team QA operations, including intake and prioritization, defect management, release readiness, and quality reporting.
• Partner with cross‑functional leaders to embed quality practices early and influence decisions that improve product quality and delivery confidence.
• Design, review, and (when needed) execute test strategies, plans, and validation approaches across products and releases.
• Lead validation of complex data and analytics solutions, including dashboards and reporting, semantic layers and metric logic, ETL/ELT pipelines and data transformations, and APIs and data integrations.
• Act as the senior QA lead on high‑risk or high‑visibility initiatives, stepping in hands‑on to ensure successful delivery.
• Establish and evolve QA standards, frameworks, and reusable templates used across teams.
• Define and enforce core QA practices (e.g., test coverage expectations, defect lifecycle, release quality criteria).
• Expand test automation and data validation automation to improve speed, coverage, and repeatability.
• Guide selection and effective use of QA tools (test management, defect tracking, API/data testing, automation).
• Use quality metrics, defect trends, and retrospectives to reduce recurring issues and strengthen release confidence.

Any combination of education and experience providing the required skill and knowledge is qualifying.

• Bachelor’s degree in computer science, information systems, or related field (or equivalent experience).
• 8+ years of progressive quality assurance experience, including substantial experience testing data and analytics solutions.
• 3+ years of people leadership and/or QA program leadership with responsibility for setting direction, managing priorities, and driving outcomes across multiple teams.
• Demonstrated expertise testing data‑centric products such as dashboards, reports, data models, ETL or ELT pipelines, APIs, and analytics platforms.
• Experience with SQL and data validation techniques, including reconciliation, source‑to‑target validation, and business rule testing.
• Experience establishing QA processes, standards, and operating models that can be used consistently across products or delivery teams.
• Experience with test management, defect tracking, API testing, and automation tools; ability to apply automation pragmatically in support of data and analytics quality goals.
• Familiarity with modern delivery practices and tools such as Azure Dev Ops, Git, CI/CD pipelines, and Agile delivery methods.
• Strong ability to partner with technical and business stakeholders, translate requirements into test strategy, and influence quality outcomes across organizational boundaries.
• Excellent leadership, communication, and organizational skills, with the ability to balance strategic oversight and direct execution.
• Preferred experience with healthcare, regulated data environments, or validation of metrics and reporting used for operational or executive decision‑making.