Enterprise Data Architect & Data Modeler

🏢Summary

The Enterprise Data Architect & Data Modeler 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.

Primary Outcomes (What Success Looks Like)

• 🔹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

✅Core Responsibilities

• ✅Enterprise Data Architecture & Modeling
• ✅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
• ✅Shared Definitions & Business Alignment
• ✅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
• ✅Data Product Enablement
• ✅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 r…