Let's begin! Director Data Strategy

At Moodys we unite the brightest minds to turn todays risks into tomorrows opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they arewith the freedom to exchange ideas think innovatively and listen to each other and customers in meaningful ways. Moodys is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment were advancing AI to move from insight to actionenabling 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 Moodys Analytics transforms governs and delivers its core data assets. This role involves driving the strategy innovation and operational excellence of Moodys 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 Moodys mission to decode risk and unlock opportunity.

Key Responsibilities

• Strategy & Vision
  • Data Strategy & Planning: Define and execute long-term strategic initiatives and a multi-year roadmap for Moodys 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 portfolios 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.

Required Qualifications

• 8 years of experience in Data Strategy or related roles with demonstrated expertise in defining and executing a multi-year product strategy for complex large-scale data assets preferably in a B2B or Enterprise environment.
• Strong understanding of modern product management and Agile methodologies (Scrum/Kanban) at a portfolio level.
• Deep expertise in understanding data content data models and data structures with the ability to oversee and guide the design and optimization of data structures across the entire product portfolio to ensure scalability usability and alignment with business needs.
• Solid technical acumen with experience overseeing data processing pipelines data warehousing technologies (e.g. Snowflake Redshift) and data governance tools.
• Exceptional written and verbal communication skills with the ability to articulate complex technical concepts to executive-level non-technical audiences.

Preferred Qualifications

• Deep expertise in the Finance Risk or Regulatory sectors with an understanding of the critical importance of data quality and timeliness in these domains.

Education

• Bachelors degree in a technical or quantitative field (e.g. Computer Science Engineering Finance or Data Science).
• An MBA or Masters degree is a plus.

Moodys 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 Moodys Corporation may be asked to disclose securities holdings pursuant to Moodys 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.