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. The Data Platform Product Manager owns a data engineering squad responsible for building and operating one of Moody’s Analytics’ core data platforms. Our data platform team has a strong bias toward rapid, reliable delivery of new data sets, while maintaining high standards for data quality, controls, and governance. This position is part of the Data Product Management Team, a dynamic, analytical group that is curious about data but relentlessly focused on driving value for customers and the business. Key Responsibilities Own a data engineering squad, accountable for roadmap planning, execution, and delivery Drive effective delivery by accelerating decision making, removing blockers, and keeping the team focused on the highest value outcomes for customers and the business Partner with the Data Platform Product Owner, Data Governance, Quality Assurance, Data Engineering, and internal Product Development teams to bring new data sets to market Collaborate closely with data engineers to understand solution complexity, evaluate architectural tradeoffs, and select implementation approaches that balance speed, scalability, cost, and AI readiness Manage agile ceremonies and execution (sprint planning, backlog refinement, reviews, retrospectives) to ensure consistent, high quality delivery Facilitate alignment across data and product development squads to manage dependencies and enable sound decision-making Own and prioritize a backlog consisting of user stories, defects, refactors, infrastructure work, and production support, prioritized by business value, platform reuse, risk reduction, and AI enablement Use AI and GenAI tools as part of day to day product management, including: Accelerating creation, refinement, and validation of product requirements, user stories, and acceptance criteria Summarizing stakeholder input, data issues, incidents, and operational metrics to inform prioritization decisions Supporting impact analysis and root cause exploration for data quality or delivery issues Identify opportunities to automate and augment data delivery workflows, including data quality validation, documentation, metadata management, and operational reporting Coordinate release readiness and deployment with Release Management, Operations, Data Quality, and Data Governance partners Build strong relationships with data strategy and business stakeholders to drive delivery of new data sets and the evolution of existing data assets Embed data quality, lineage, and governance standards directly into backlog items and acceptance criteria, with particular focus on data used by AI and model driven products Proactively identify and mitigate delivery, quality, privacy, and operational risks throughout the data lifecycle, particularly where AI usage increases sensitivity, scale, and downstream impact Qualifications Bachelor’s degree required; advanced degree (MBA, Master’s) a plus 5+ years of experience in technical product management or data engineering for data intensive B2B products Strong grasp of agile methodologies and delivery practices Prior experience in data platform, data warehousing, or analytics environments (e.g., Snowflake, Databricks, Redshift, Athena, Kafka) Experience defining, monitoring, and operationalizing data quality/QA standards, especially for downstream analytics or AI consumers Experience using AI or Generative AI tools to improve productivity in product management, including requirements authoring, analysis, summarization, and decision support. Proficiency with SQL for data analysis, troubleshooting, and validation Familiarity with how data is used by machine learning or AI systems, even if not directly building models Results oriented and action focused, with experience driving delivery in complex, matrixed environments Experience working in matrixed organizations, including teams supported by vendors or external partners Strong communication skills with the ability to translate complex data and platform concepts for technical and non technical stakeholders Strong analytical skills, persistence in problem-solving, and attention to detail Demonstrated initiative, curiosity, and commitment to continuous improvement A track record of improving team and organizational effectiveness through influence and scalable processes Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.