Enterprise Data Architect

Reports to: Director – Data Trust & Context

Location: Arcadia, WI;
Advance, NC; or Tampa, FL

The Enterprise Data Architect defines and enables the shared data foundation that ensures consistent understanding of core business entities across the enterprise.

This role translates business concepts into reusable enterprise models, authoritative definitions, and contextual frameworks that accelerate decision-making, improve data product delivery, and ensure AI and analytics solutions operate on trusted, aligned data
.

By establishing a common language for data, this role reduces ambiguity, strengthens cross-domain alignment, and enables teams to deliver faster with confidence.

• Business-critical data is consistently defined and understood across teams
• Data products, analytics, and AI solutions are built on trusted, aligned definitions
• Cross-domain data conflicts are resolved quickly with clear decisions
• Teams spend less time reconciling data and more time delivering business value
• Enterprise data context is discoverable, reusable, and embedded in delivery workflows

• Define and evolve enterprise conceptual and logical data models for key business domains
• Establish clear representations of business entities, relationships, and domain boundaries
• Provide architectural guidance that promotes consistency while supporting team autonomy
• Apply enterprise modeling and naming standards through practical guidance and review
• Identify gaps in standards based on real-world usage and drive continuous improvement

• Develop and maintain authoritative definitions for cross-domain business concepts
• Enable a common business language that supports reporting, analytics, and AI
• Identify risks where inconsistent definitions could impact business outcomes and drive stakeholder alignment to resolve those conflicts

• Partner with Data Product Management and delivery teams to embed enterprise context into design
• Ensure data context is usable and reliable for AI, analytics, and semantic layers
• Ensure alignment between data products and enterprise models without slowing delivery
• Provide guidance that improves delivery speed and reduces rework
• Support the operationalization of data context in catalogs, semantic layers, and data products
• Enterprise models for priority domains and shared concepts
• Approved definitions for key business entities and metrics
• Resolution of cross-domain data definition conflicts
• Architectural guidance that improves alignment across data products
• Reusable, published data context for enterprise consumption

This role does not own physical data design, pipelines, or delivery execution.

The focus is on enterprise data architecture, shared meaning, and cross-domain alignment
.

• 5+ years of experience in enterprise data modeling, data architecture, information architecture, semantic modeling, or a closely related discipline.
• Demonstrated experience developing conceptual and logical data models across multiple business domains.
• Strong ability to translate business language into precise definitions, entities, relationships, and reusable data structures.
• Experience facilitating cross-functional alignment with business SMEs, data architects, analysts, engineers, product managers, and governance stakeholders.
• Familiarity with metadata management, data catalogs, data governance, data products, and data quality concepts.
• Strong written communication skills, including the ability to produce clear model documentation and decision records.

• Experience with enterprise modeling tools, metadata repositories, catalog platforms, or knowledge graph / ontology patterns.
• Experience with domain-driven design, master data, reference data, canonical models, semantic layers, or data contracts.
• Familiarity with AI grounding, RAG, vector search, or ontology integration patterns.
• CDMP, DAMA, TOGAF, or comparable data architecture / data management certification.

• Adoption of enterprise models and definitions across teams
• Reduced time spent resolving data inconsistencies
• Improved speed and quality of data product and analytics delivery
• Increased confidence in data used for business decisions and AI