Senior Data & Analytics Governance Analyst

TriHealth•Cincinnati, OH

1d•Remote

About The Position

The Senior Data & Analytics Governance Analyst supports enterprise analytics and data governance initiatives by ensuring critical data assets are high quality, well documented, secure, and optimized for advanced analytics and AI use. Working across platforms such as Epic Cogito and Microsoft Fabric, this role implements and maintains governance frameworks, policies, and standards within a business-aligned, domain-oriented model to promote consistent, "AI-ready" data. The position partners closely with business, technical, and data stewardship teams to enhance data quality, metadata, and stewardship practices; strengthen data ownership and accountability; and enable trusted, compliant use of data in alignment with organizational and regulatory requirements. The position reports directly to the Data Warehouse Manager. Key responsibilities include supporting the analytics and data governance roadmap; coordinating governance standards, definitions, and stewardship practices; helping maintain inventories of critical information assets and authoritative sources; facilitating adoption of data quality and data protection standards; monitoring governance metrics and policy adherence; partnering across business and technology teams to clarify requirements and resolve issues; and contributing to governance reporting, process improvement, and implementation efforts. The role emphasizes collaboration, execution, analysis, and influence rather than direct enterprise-wide leadership accountability. Consistent with current leading practice, the role supports a business-led governance model in which governance establishes decision rights, accountability, policy support, and measurable business outcomes, while technical teams enable execution through data quality, metadata, and reporting capabilities.

Requirements

Bachelor's Degree in the field of computer science or information systems or business administration (Required)
3 - 4 years Experience in healthcare or a related field supporting analytics, data governance, AI governance, data quality, or cross-functional initiatives in a complex environment. (Required)
2 - 3 years Experience working with business stakeholders to document requirements, support governance processes, assess risk, and improve data-related and AI-related decision making . (Required)
3 - 4 years Experience with analytics, data management, governance processes, AI oversight, reporting, or project coordination in a large-scale environment. (Required)
Knowledge of data quality, metadata, stewardship, and data protection practices
Knowledge of data governance frameworks, standards, and stewardship practices
Knowledge of data protection, privacy, and appropriate data use practices
Knowledge of applicable regulatory and organizational requirements related to data and analytics
Ability to support governance processes using reporting, documentation, and data management tools
Knowledge of AI risk, model documentation, third-party AI considerations, and appropriate use of AI-enabled solutions
Knowledge of AI governance principles including transparency, accountability, human oversight, and lifecycle controls
Epic Certification Clinical/Access/Revenue Data Model Certification including all prerequisites & Cogito Tools Administration (other certifications as needed) within 1 Year Required
Other Azure - AZ900, AI900 (other certifications as needed) within 1 Year Required

Responsibilities

Supports standardization of enterprise data definitions, business terms, AI-related metadata, and governance documentation to improve consistency, reuse of data assets, and self-service access to trusted insights.
Coordinates with business, clinical, technical, data stewardship, and AI governance stakeholders to support the availability, usability, integrity, security, and appropriate use of data and AI-enabled assets.
Supports the quality, stability, and readiness of data used in analytics, reporting, decision support, and AI-enabled solutions.
Contributes to project, data governance, and AI governance workstreams by helping track scope, timelines, deliverables, risks, decisions, and issue resolution.
Helps establish and maintain processes for monitoring data quality, governance adoption, AI governance controls, policy compliance, appropriate use, and the business value of governance activities.