Director Data Governance Manager

At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

Skills and Competencies

• 8+ years of relevant professional experience in data governance, data stewardship, data quality, master and reference data, data controls, or product data management, including leading teams and complex cross-functional initiatives
• Deep knowledge of business-entity and relationship data, including subsidiaries and corporate hierarchies, shareholders and beneficial ownership, persons, officers and directors, news and media, sanctions and watchlists, and other relationships among legal entities and individuals
• Strong command of data governance and stewardship practices consistent with DAMA-DMBOK and EDM Council DCAM, including ownership and decision rights, policies and standards, critical data elements, business definitions, metadata, lineage, lifecycle management, issue management, and change control
• Proven ability to design and operationalize data quality controls, including defining quality dimensions and thresholds, designing executable tests, selecting control points within ETL and data pipelines, enabling human-in-the-loop exception management, and defining remediation, root-cause analysis, and closure criteria, supported by working knowledge of data catalogs, data quality platforms, workflow and ticketing tools, SQL, ETL technologies, and cloud data platforms
• Strong product, customer, and commercial orientation, with the ability to translate customer use cases and product needs into measurable data requirements and to connect data quality, coverage, and provenance to product differentiation, customer value, revenue, and commercial strategy
• Demonstrated leadership in people management, program management, and change management, including directing teams and matrixed partners, transitioning new controls into sustainable operations, and communicating complex issues, trade-offs, residual risk, and investment needs to technical and non-technical audiences across multiple layers of the organization
• Deep expertise in and genuine enthusiasm for artificial intelligence, with a track record of championing AI adoption and embedding AI into data quality assurance and audit workflows to detect data errors at scale, including profiling, anomaly detection, relationship and ownership-structure analysis, and prioritization of exceptions for human review, while measuring detection accuracy and reducing false positives over time
• Demonstrated leadership in using AI to correct data errors in controlled and governed ways, including AI-assisted correction, enrichment, and remediation recommendations with defined confidence thresholds, human-in-the-loop approval, audit trails, and validation before publication, and in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across stewardship and operations teams

Education

• Bachelor’s degree in Data Management, Information Systems, Computer Science, Business, Finance, Economics, or a related field required; equivalent professional experience may be considered
• Advanced degree or relevant professional certification, such as CDMP, DCAM, data quality, project management, process improvement, or change management, preferred

Responsibilities

Lead governance, quality controls, and stewardship of business-entity relationship data, from customer requirements through sustained production use.

• Lead the stewardship and day-to-day governance of business-entity relationship data domains, establishing business definitions, data-element standards, critical data elements, ownership, decision rights, permitted values, sourcing expectations, and lifecycle rules, and chairing domain governance forums to resolve cross-functional data decisions
• Partner directly with customers, Product Management, Product Development, Sales, Client Service, Commercial Strategy, and peer data stewards for other data domains to understand how data is used, define fitness-for-use expectations, and translate customer evidence, product performance, and commercial outcomes into governed requirements and investment priorities
• Define data quality rules, thresholds, and acceptance criteria aligned with approved