Let's begin! Director of Data Governance - Data Modeling

Moody's Corporation•Charlotte, NY

About The Position

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. Employment eligibility to work in the U.S. is required, as Moody’s will not pursue visa sponsorship for this position now or in the future

Requirements

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
Basic understanding of artificial intelligence concepts, with curiosity and enthusiasm for learning how AI tools can be used to improve processes and drive efficiency.
Interest in exploring AI systems and a willingness to develop awareness of responsible AI practices, including risk management and ethical use

Responsibilities

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 domains.
Ensure that data quality rules are firmly rooted in the semantics of the data model, and review QA approaches to guarantee alignment with modeling intent rather than just technical implementation
Ensure the Data Estate Logical Data Model is AI-ready and interoperable by supporting stable identifiers, explicit relationships, and rich, explainable semantics.
Define and implement modeling standards necessary for advanced AI applications such as Retrieval-Augmented Generation (RAG), Generative AI, and agentic workflows, proactively identifying and addressing model gaps that could lead to hallucinations, misinterpretations, or incorrect reasoning, in collaboration with governance and platform teams
Collaborate with stakeholders to resolve conceptual conflicts among domains, products, and legacy systems.
Ensure all changes and their rationale are communicated effectively to Data Stewards, Engineering, Product, and customer-facing teams
Establish strong and collaborative relationships with business partners to help identify gaps in existing data models and design new data models to support application interfaces, workflows and monitoring and reporting usage