Associate Director – Data Platform Product Manager

The Data Platform Product Manager leads a data engineering squad that builds and operates core data platforms, prioritizing rapid, reliable delivery of new data sets while upholding high standards for data quality, controls, and governance.

This role is part of the Data Product Management Team, a dynamic, analytical group focused on delivering customer and business value through data.

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 trade‑offs, 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 in 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.

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