We are seeking a Senior Data Modeler to define, evolve, and govern our enterprise-wide North Star Data Model. This role will make data consistent, interoperable, discoverable, and reusable across business domains, products, analytics, and AI use cases. 

The Senior Data Modeler will work with Product, domain experts, Data Engineering, Analytics, Architecture, Governance, and Security to translate business concepts into a shared enterprise data language and practical models that teams can implement across the Bronze, Silver, and Gold layers of our data platform. 

This is not a role focused only on designing tables for individual projects. It owns the connective tissue between domain models: the common entities, identifiers, relationships, definitions, metadata, and standards that allow trusted data products to work together across the enterprise. 

What you will do 

• Establish the enterprise North Star Data Model 
• Define and maintain enterprise conceptual, logical, and physical data models across core business domains. 
• Establish canonical business entities, shared dimensions, reference data, identifiers, relationships, and lifecycle states. 
• Create a pragmatic model that supports domain autonomy while enabling cross-domain analysis and data sharing. 
• Maintain an enterprise ontology, business glossary, and semantic definitions so that important terms and metrics mean the same thing across products and teams. 
• Design for reuse, extensibility, regional variation, and future use cases while avoiding unnecessary centralization. 
• Model the Bronze, Silver, and Gold data layers 
• Define modeling principles and required artifacts for each layer: 
  • Bronze: Preserve source fidelity, source lineage, ingestion metadata, auditability, and raw-data contracts. 
  • Silver: Standardize and validate data; apply common identifiers, canonical entities, conformed dimensions, data-quality rules, and cross-domain integration patterns. 
  • Gold: Deliver governed, business-ready data products, semantic models, certified metrics, and analytics-ready structures for dashboards, self-service analysis, and AI. 
• Ensure traceability from Gold metrics and business concepts back through Silver transformations to authoritative Bronze sources. 
• Define clear rules for when a concept belongs in a domain model, the shared enterprise model, a semantic layer, or a product-specific analytical model. 
• Review and guide physical implementations for performance, maintainability, cost, privacy, and scale. 

Enable data products and delivery teams 

• Partner with Product Managers and domain leaders to turn business outcomes and use cases into clear data-modeling requirements. 
• Partner with Data Engineers to define schemas, transformations, mappings, data contracts, and implementation patterns. 
• Partner with BI developers to create governed semantic models, reusable measures, and self-service-ready datasets. 
• Facilitate architecture and design reviews; identify duplication, inconsistent definitions, broken lineage, and integration risk early. 
• Provide model patterns, templates, and coaching that enable teams to deliver independently while following enterprise standards.
• Govern for trust, security, and global scale 
• Embed data quality, lineage, ownership, retention, security classification, privacy-by-design, and access-control requirements into data-model designs. 
• Model regional, regulatory, language, currency, and local-business variations without fragmenting global reporting or shared concepts. 
• Define stewardship and decision rights for enterprise entities, metrics, and reference data. 
• Maintain model documentation and metadata in the organization’s data catalog and modeling tools. 
• Measure adoption, reuse, quality, coverage, and exceptions to drive continual improvement of the North Star model. 

What success looks like 

• Teams use the same definitions for shared business concepts and key metrics. 
• New data products can be delivered faster because common models, dimensions, identifiers, and patterns are reusable. 
• Cross-domain reporting is reliable, explainable, and traceable to authoritative sources. 
• Data consumers can discover what data exists, what it means, who owns it, and whether it is fit for use. 
• Regional expansion is supported through intentional extensions rather than isolated local models. 
• Gold-layer analytics and AI experiences use certified semantic definitions rather than reconstructing logic independently.