Data Product Manager

The National Multiple Sclerosis Society is dedicated to driving research for a cure for MS and addressing the challenges faced by those affected. The Data Product Manager will develop business data models and frameworks to enable effective analytics and decision-making across the organization.

Responsibilities:

• Lead the design, development, and evolution of shared business data models, metrics, and reusable reporting structures that support consistent and scalable analytics
• Translate business processes and requirements into scalable, business-friendly data models, metric definitions, and reporting logic that enable actionable insights and decision-making
• Define and maintain standardized business definitions, metric logic, and data relationships while identifying and resolving inconsistencies that impact analytics usability, alignment, and reporting accuracy
• Collaborate with data engineering, architecture, analytics, and product teams to align enterprise data structures with semantic models, ensuring scalability, performance, and usability
• Drive adoption of shared business data models and reporting structures by improving data accessibility and enabling intuitive, scalable self-service analytics
• Improve reporting consistency and trust in enterprise data by reducing duplicate or conflicting metric definitions, supporting faster and more confident business decision-making
• Facilitate cross-functional alignment by bringing together business, analytics, and technology teams to standardize metrics and build shared understanding of enterprise data
• Provide subject matter expertise and guidance on data modeling, metric design, semantic layer best practices, and data enablement strategies
• Evaluate and evolve semantic modeling tools, frameworks, and practices to support organizational growth, changing business needs, and emerging analytics capabilities
• Manage semantic and business-facing data assets as products, including lifecycle ownership, usability, adoption, and continuous alignment with evolving business and analytics needs
• Lead cross-functional semantic modeling and analytics enablement initiatives, driving alignment, influencing approaches, and guiding teams toward scalable, business-friendly data solutions
• At this career level, you are leading in this way: by acting as a knowledge leader in semantic modeling and enterprise data design, defining best practices and guiding others in the effective and consistent use of business data

Requirements:

• Demonstrated experience in data modeling and semantic layer design, including dimensional modeling, business-facing data structures, and enterprise reporting solutions
• Experience developing and managing business metrics, definitions, and metric frameworks across multiple functional areas to support consistent analytics and reporting
• Proven ability to translate business processes and requirements into scalable data models and actionable insights, partnering with stakeholders across business, analytics, and technology teams
• Understanding of enterprise analytics, business intelligence, and self-service reporting concepts, with experience enabling scalable data access and usability
• Strong collaboration and facilitation skills, with experience driving cross-functional alignment, standardization, and shared understanding of data and metrics
• Excellent communication skills, with the ability to clearly explain complex data and technical concepts to non-technical audiences
• Strong analytical, problem-solving, and organizational skills, with the ability to resolve data inconsistencies and balance strategic design with operational execution
• Experience supporting adoption of shared business data models, metric frameworks, or enterprise reporting solutions across diverse teams
• Demonstrated ability to define and scale shared business data models or metric frameworks across multiple teams or functional areas
• Experience with semantic modeling tools, BI platforms, enterprise reporting tools, or data catalog/search tools
• Familiarity with modern data architectures (e.g., data warehouses, lakehouses, distributed systems)
• Experience contributing to enterprise data standardization or modeling initiatives
• Knowledge of data visualization best practices and analytics user experience design
• Experience driving self-service analytics adoption or business-facing data enablement
• Experience working in product-based, agile, or matrixed organizational environments
• Experience supporting domain-specific data environments such as CRM, revenue operations, fundraising, or similar operational systems