Senior Data Analytics & AI Strategy Lead

Moody's; New York (NY); On-site; USD 143,000 - 207,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Parental leave

Paid time off

401(k) plan

Life insurance

Disability insurance

Employee stock purchase plan

Tuition reimbursement

Job summary

Moody's is transforming how the world sees risk and advancing AI to move from insight to action. Moody's seeks a senior BI analyst to design trusted analytics, data products, and AI-enabled solutions for the Customer Operations and Risk organization, partnering with BI, FP&A, Sales Planning and Analytics teams.

You will translate complex business problems into clear user stories, define data definitions, and support end-to-end testing while upholding data governance and quality.

Qualifications

• 10+ years of business analysis, requirements gathering, and stakeholder management experience in business intelligence, analytics, or data product delivery.
• Demonstrated ability to translate ambiguous business problems into structured requirements, user stories, functional specifications, and testable acceptance criteria.
• Strong partnership skills across business and technology teams with the ability to influence design decisions and recommend practical, business-aligned solutions.
• Experience designing business-aligned data products, semantic models, reporting frameworks, and reusable analytics solutions in partnership with engineering teams.
• Knowledge of data modeling, Lakehouse and data warehousing concepts including dimensional modeling, semantic layers, data quality, lineage, and governance.
• Experience with Databricks, Microsoft Power BI, or comparable analytics platforms; familiarity with CRM and analytical source systems such as Salesforce is preferred.
• Strong analytical mindset with the ability to validate data, challenge assumptions, and ensure reporting accuracy and usability.
• Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency.

Responsibilities

• Partner with business and technology teams to design and deliver trusted analytics, reporting, data products, and AI-enabled solutions.
• Partner with Business Intelligence, Financial Planning and Analysis, Sales Planning and Analytics, and other stakeholders to elicit, clarify, and prioritize reporting, analytics, and data product requirements.
• Translate business problems into clear user stories, business requirements, data definitions, and acceptance criteria.
• Act as an architectural partner by evaluating and recommending the best approach across Databricks, reporting layers, analytical workflows, and AI-enabled solutions.
• Facilitate workshops and working sessions with business and technical stakeholders while managing expectations, trade-offs, and delivery scope.
• Support data modeling activities aligned to business semantics including dimensions, measures, hierarchies, grain, and lineage requirements.
• Contribute to reporting design, source‑to‑target mapping, semantic layer development, reusable data products, and data consumption patterns.
• Lead or support end‑to‑end testing of reports, datasets, and analytical outputs, validating results against source systems and business rules.
• Ensure solutions meet standards for data quality, accuracy, governance, usability, and identify opportunities for AI‑driven insights, automation, and decision support.

Skills

Business analysis

User stories

Stakeholder management

Data products design

Data modeling

AI concepts

Analytical mindset

Education

Bachelor's degree

Tools

Databricks

Microsoft Power BI

Job description

Moody's is transforming how the world sees risk and advancing AI to move from insight to action. Moody's seeks a senior BI analyst to design trusted analytics, data products, and AI-enabled solutions for the Customer Operations and Risk organization, partnering with BI, FP&A, Sales Planning and Analytics teams.

You will translate complex business problems into clear user stories, define data definitions, and support end-to-end testing while upholding data governance and quality.