Let's begin! Sr Data Governance Analyst

Moody's•Charlotte, NC

About The Position

The Sr Data Governance Analyst leads and contributes to defining, managing, and governing data across product workflows to support digital transformation and AI-enabled use cases across Moody’s Ratings. The role leads data readiness for intelligence domains and digital and AI-enabled use cases, harmonizes data requirements and governance, assesses data sources, connects and validates data across domains, and oversees lineage, metadata, quality, and migration activities. It translates prioritized use cases into delivery requirements and roadmaps, collaborates with Product Owners and cross-functional Agile teams, and provides guidance, training, communications, and rollout support for data, technology, and AI-related changes. The position is part of the Ratings Process and Data team within Moody’s Ratings Platform Development organization. The team drives data and process design and change management for scalable digitization, including workflows, analytics, ESG services, and generative AI adoption. It works in an Agile, product-centric model with stakeholders across business lines, Product Management, User Experience, development, and control functions. The work includes documenting current-state processes, defining requirements, mapping data elements, and leading simplification initiatives, with exposure to senior leaders across business and technology.

Requirements

At least 5 years of demonstrable experience in data management and data transformation roles.
Proficiency in SQL, including querying, joining, profiling, reconciling, and validating data across relational databases.
Experience using Master Data Management and Data Governance tools such as dBeaver, Access, Collibra, and Microsoft Purview.
Experience creating and/or maintaining data dictionaries and working with database structures, data lineage mapping, and data governance reporting.
Experience working with unstructured data and profiling and integrating data from multiple internal and third-party sources for analytics, data products, AI readiness, or intelligence-domain use cases.
Experience working with customers and/or users to understand data requirements and use cases.
Knowledge of data governance roles and frameworks and understanding of Agile methodologies.
Proficient communication skills, including the ability to simplify complex themes for cross-functional audiences.
Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency.
Proven ability to implement AI-powered solutions to solve business challenges.
Growing awareness of AI risk management and commitment to responsible and ethical AI use.

Nice To Haves

Bachelor’s degree in Data Science, Computer Science, Information Systems, or a related field is preferred; equivalent professional experience will be considered.

Responsibilities

Lead data readiness for intelligence domains and digital and AI-enabled use cases by inventorying, organizing, documenting, cleansing, validating, and assessing priority analytical, reference, market, and workflow datasets.
Define and govern harmonized data requirements, concepts, taxonomies, attributes, reference data, and business rules to improve consistency, usability, and quality across Moody’s Ratings.
Discover and assess internal and third-party data sources, documenting business context, ownership, consumers, licensing, access, refresh needs, dependencies, and fitness for purpose.
Profile, reconcile, and connect data across domains, resolving entity, instrument, identifier, hierarchy, mapping, and coverage gaps in collaboration with Technology and subject-matter experts.
Oversee end-to-end data lineage, source-to-target mappings, metadata, and authoritative-source rules to ensure transparency, traceability, and controlled downstream use.
Lead data quality assessments and issue management, using defined measures to identify patterns, gaps, duplicates, and inconsistencies and drive remediation with accountable owners.
Translate prioritized use cases into build-ready requirements and roadmaps for ingestion, data models, pipelines, dashboards, reusable analytical assets, and modern cloud-based infrastructure.
Lead data rationalization and migration activities, including comparison of legacy content with target data structures, validation of migrated data, and controlled retirement of redundant content.
Partner with Product Owners and cross-functional teams in Agile delivery models to challenge existing approaches, resolve data issues, and deliver incremental improvements to data products and workflows.
Provide guidance to analysts and partners on data governance standards and delivery expectations, and lead training, communications, and rollout activities for data, technology, and AI-related changes.