Director, Data Quality

IQVIA•Burlington, MA

3d•Hybrid

About The Position

Cedar Gate Technologies, an IQVIA business, is hiring a Director of Data Quality to lead data quality strategies within a complex healthcare data environment, and to modernize data quality processes using Agentic AI and intelligent automation. This role ensures healthcare data is accurate, complete, timely, and fit for operational, regulatory, and analytical use. The Director will provide leadership across data quality management, governance, reporting integrity, data stewardship, and process improvement. This role serves as a Subject Matter Expert in Agentic AI frameworks and AI-driven data quality capabilities, responsible for advancing the organization’s use of automation and AI to scale and modernize data quality operations.

Requirements

10+ years of experience in data quality, data management, data governance, and data operations
5+ years of leadership experience managing teams or enterprise data initiatives
Hands-on experience with AI agents, agentic frameworks, or AI-enabled automation
Experience implementing agentic workflows or intelligent automation
Demonstrated experience implementing data quality frameworks, controls, and monitoring capabilities
Bachelor’s degree in relevant fields such as Data Management, Computer Science, Healthcare Administration, or Analytics
Experience applying AI to data quality processes such as monitoring, anomaly detection, or remediation for enterprise data operations is highly preferred
Experience working with data pipelines, data platforms, or analytics ecosystems
To be eligible for this position, you must reside in the same country where the job is located.

Nice To Haves

Master’s degree preferred
Experience in healthcare payer, healthcare provider, or healthcare technology organizations is preferred
Knowledge of healthcare standards (FHIR, HL7, HEDIS, ICD, etc.) will enhance your candidacy.
Experience with data governance tools, data quality platforms, or data catalogs will help you be successful in the position.
Familiarity with responsible AI practices and AI governance frameworks will reduce your learning curve.

Responsibilities

Develop and lead the enterprise-wide Data Quality strategy aligned to business goals and regulatory requirements
Establish data quality standards, policies, and operating models
Define and monitor key dimensions including accuracy, completeness, consistency, timeliness, and integrity
Implement data quality scorecards, dashboards, and executive reporting
Serve as the enterprise leader for data quality practices
Serve as a Subject Matter Expert in Agentic AI frameworks and AI agents, leading the design and implementation of AI-enabled data quality capabilities across the enterprise, including data profiling, anomaly detection, validation, monitoring, root cause analysis, and automated issue triage and remediation recommendations
Define and operationalize AI use cases within the Data Quality framework, ensuring alignment with business priorities, regulatory requirements, and enterprise risk standards
Establish and enforce governance standards for AI-driven data quality processes, including human oversight, explainability, and auditability, ensuring responsible and compliant AI adoption
Partner with Data Engineering, AI/ML, and Architecture teams to deploy scalable, production-grade AI-enabled data quality solutions across data platforms and pipelines
Lead teams responsible for identifying, prioritizing, and resolving data quality issues
Establish repeatable processes for profiling, root cause analysis, and remediation tracking
Embed data quality controls into data pipelines, platforms, and reporting environments
Leverage automation and AI to reduce manual effort and improve efficiency
Drive continuous improvement initiatives across data quality processes
Partner with leaders across Operations, Clinical, Compliance, IT, Analytics, and Product
Translate business needs into data quality requirements and AI-driven solutions
Drive alignment on data ownership, definitions, and governance practices
Communicate complex data and AI concepts to executive and non-technical stakeholders
Lead and develop a high-performing team across data quality, governance, and data operations
Establish performance metrics, goals, and career development plans
Build capabilities in AI-enabled data quality, automation, and modern data management practices