Director of Data Governance - Data Modeling

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. If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.

Skills and Competencies

• Experience working with Agile product development processes.
• Prior technical business analyst or technical product management level knowledge.
• Prior experience analyzing and/or designing relational and non-relational data sets, especially in financial data, commercial real estate data, public registration and regulatory data, structured and semi-structured news data, and the relationships between entities in such datasets.
• Experience and proficiency using SQL for data analysis, or similar AI enabled platforms.
• Experience partnering with Legal, Risk, and Data Governance stakeholders to ensure data privacy compliance and proactively mitigate regulatory, operational, and data misuse risks.
• Experience working in a matrixed environment and navigating multiple stakeholders to drive alignment.
• Outstanding verbal and written communication skills.
• Superb analytical skills and persistence in problem solving.
• Demonstrated initiative, enthusiasm to learn, excel and be a part of a dynamic team.

Education Undergraduate/first-level degree (e.g., Bachelor’s) or graduate/second-level degree (e.g. MBA, Master’s) with an emphasis in finance, economics, computer science, data science, or related fields.

Responsibilities

The Director of Data Governance – Data Modeling will play a pivotal role within Moody’s Analytics Data Estate Data Governance team. This position is responsible for designing, coordinating the review process, and maintaining comprehensive documentation for data models at all levels—conceptual, logical, and physical. The Director will collaborate closely with technology and application development teams to ensure data models are well-documented, accurate according to the policies and standards set by Data Stewards, and consistently updated to support evolving business and customer requirements. This role enables teams to better understand and utilize foundational data structures, by fostering transparency and clarity in data modeling documentation, ultimately supporting data-driven decision-making and operational excellence.

• Serve as the accountable owner of the Data Estate Logical Data Model (LDM) across all domains, maintaining a single, authoritative source for entities, relationships, attributes, and their justifications. Ensure the LDM accurately reflects customer and product perspectives, and manage all changes through a governed, auditable process.
• Translate business use cases into scalable modeling requirements by reviewing and validating workflows provided by Data Stewards, abstracting their inputs into reusable modeling patterns, and identifying common entities, standardized relationships, and consistent attribute definitions across domains.
• Maintain conceptual integrity by ensuring entities and classifications are consistent across data stores and consumption points such as data feeds, API endpoints, and user interfaces. Address structural modeling issues like duplicate concepts, inconsistent use of entity types and attributes, and overloaded fields representing multiple real-world concepts.
• Define and manage the relationship model by establishing clear types, cardinality, directionality, and temporal behavior, ensuring relationships accurately represent real-world constraints rather than system limitations. Prevent inconsistencies across domains and maintain thorough justifications so relationships can be clearly explained to customers, withstand audits, and be reliably utilized by AI systems.
• Establish modeling standards for Data Stewards by defining clear criteria for steward-proposed changes, publishing guidelines on entity creation, attribute versus entity decisions, and relationship semantics, and rigorously reviewing outputs for structural accuracy, alignment with enterprise patterns, and long-term scalability.
• Provide Data Quality teams with guidance on critical entities, essential relationships and attributes across

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