Director - Data Strategy

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 Director of Data Strategy will be a key contributor responsible for defining and executing the vision for how Moody's Analytics transforms, governs, and delivers its core data assets. This role involves driving the strategy, innovation, and operational excellence of Moody's data product portfolio, collaborating with cross-functional teams, and ensuring the highest standards of data quality, scalability, and value delivery.
The successful candidate will bring deep expertise in data strategy, set best practices for data excellence, and champion a culture of data-centric product development that directly supports Moody's mission to decode risk and unlock opportunity.

• Strategy & Vision
  • Data Strategy & Planning:
    Define and execute long-term strategic initiatives and a multi-year roadmap for Moody's data product portfolio, ensuring alignment with business goals and technology architecture.
  • Innovation & AI:
    Identify and evaluate emerging technologies, including AI/ML capabilities, to enhance data assets, streamline delivery, and unlock new value for both internal and external stakeholders.
  • Data Discovery:
    Partner with stakeholders to discover, evaluate, and onboard new data sources to expand the portfolio’s value and relevance for business needs.
• Portfolio Execution
  • Data Product Requirements:
    Lead the creation and ownership of detailed data briefs and technical requirements to define and guide data querying, transformation, enrichment, and delivery processes. Ensure all requirements are aligned with business objectives, application needs, and end-user expectations, enabling the development of scalable, high-quality data products that deliver measurable value.
  • Data-Driven Decision-Making:
    Collaborate with business units to identify key performance indicators (KPIs) and develop data-driven frameworks for decision-making.
  • Delivery Assurance:
    Collaborate with product, engineering, architecture, and application teams to ensure successful execution of data initiatives, meeting performance, scalability, and quality standards.
  • Risk Management:
    
    Proactively identify and mitigate risks and dependencies across data product initiatives, ensuring transparency and clear escalation paths to senior leadership.
• Governance & Quality
  • Data Governance:
    Advocate for and enforce rigorous standards of data quality, metadata management, data lineage, and compliance across the portfolio.
    Scalability & Architecture:
    Partner with Data Engineering and Data Architecture teams to ensure the data infrastructure is secure, scalable, and aligned with future business needs.
  • Automation & Monitoring:
    Drive the adoption of automation and monitoring tools to enhance operational efficiency, reduce failure points, and optimize the end-to-end data lifecycle.
• Stakeholder Collaboration & Communication
  • Executive Communication:
    Provide clear, concise updates on data product strategy, progress, and risks to senior and executive stakeholders.
  • Cross-Functional Partnership:
    Collaborate closely with Product Strategy, Product, Engineering, QA, and Business teams to ensure seamless integration of data assets into delivery platforms.