Data Modeling Director

The Director, Enterprise Data Modeling & Architecture Governance is a senior technology and data leader responsible for establishing and advancing the enterprise approach to data modeling and ensuring that data architecture standards are consistently governed and adopted across the organization.

This role will define the enterprise data modeling strategy, standards, patterns, automation and data architecture governance framework across conceptual, logical, and physical data models and enterprise data capabilities. The Director will partner closely with Enterprise Data Architecture, Data Engineering, Data Governance, business data leaders, and application architecture teams to ensure that data is designed as a reusable, interoperable, well-governed enterprise asset.

The role will provide leadership across modern data ecosystems, including Snowflake, AWS, operational platforms, analytical environments, and data products, while establishing governance mechanisms that balance enterprise consistency with the speed required by delivery teams.

A critical responsibility will be connecting business semantics, enterprise information models, data products, and physical implementation, ensuring that enterprise data architecture is translated into actionable standards for engineering teams.

Primary Responsibilities

Enterprise Data Modeling Strategy

• Define and own the enterprise strategy for conceptual, logical, and physical data modeling.
• Establish common enterprise modeling methodologies, standards, naming conventions, patterns, and reusable reference models.
• Develop and maintain enterprise canonical models and shared business entities for critical data domains as applicable.
• Establish clear relationships between business concepts, business glossaries, logical models, physical models, APIs, data products, and analytical datasets.
• Drive adoption of domain-oriented modeling approaches supporting both operational and analytical use cases.

Enterprise Data Architecture Governance

• Establish governance processes to ensure data solutions conform to approved enterprise architecture principles and standards.
• Lead or provide senior leadership to data architecture review and design governance forums for data-related initiatives.
• Define practical data architecture guardrails and decision criteria that engineering teams can incorporate into solution development.
• Establish mechanisms for evaluating and documenting architecture exceptions, technical debt, and remediation plans.
• Partner with Enterprise Architecture and solution architects to ensure data architecture decisions are consistent across business and technology domains.
• Introduce measurable governance practices, including architecture compliance, model reuse, exception aging, and standards adoption.

Metadata, Governance & Semantic Architecture

• Integrate data modeling with the enterprise metadata and governance ecosystem.
• Ensure alignment among data models, business glossaries, data catalogs, metadata, lineage, and data-quality controls.
• Partner with Data Governance and Data Stewardship teams to establish consistent definitions for critical data elements.
• Drive increased automation between modeling, metadata-management, governance, and engineering processes where appropriate.
• Advance semantic modeling capabilities that make organizational data easier to discover, understand, govern, and use by both people and AI.

Modern Data Architecture

• Provide architectural leadership for modeling across Snowflake and AWS-based data ecosystems, as well as traditional and operational data platforms.
• Establish modeling guidance covering relational, dimensional, event-driven, semi-structured, and analytical data patterns.
• Define architectural approaches for data sharing, integration, interoperability, and appropriate separation between operational, analytical, and data-product concerns.
• Partner with Data Engineering leadership to translate logical architecture into scalable physical implementations.
• Evaluate emerging approaches including knowledge graphs, semantic layers, metadata-driven architecture, data contracts, and AI-enabled data management.

Leadership & Organizational Influence

• Build and lead a high-performing enterprise data modeling and architecture governance capability.
• Develop senior data modelers while establishing clear career paths and communities of practice.
• Influence senior technology and business leaders on enterprise data architecture decisions.
• Facilitate resolution of architecture decisions that span multiple organizations or data domains.
• Balance strategic architecture objectives with delivery timelines, investment priorities, risk, and operational constraints.
• Establish multi-year roadmaps for improving enterprise modeling maturity and reducing structural data complexity.

Talent Needed for Success

Technical & Architecture Expertise

• 12+ years of experience in data architecture, data modeling, data engineering, information architecture, or related disciplines, with significant experience operating at enterprise scale.
• 5+ years of leadership experience managing architects, modelers, or senior technical professionals.
• Deep expertise in conceptual, logical, and physical data modeling.
• Strong understanding of enterprise information architecture, canonical models, semantic modeling, dimensional modeling, normalization, and domain-driven design.
• Demonstrated experience establishing enterprise architecture governance frameworks, standards, and review processes.
• Experience designing modern cloud data architectures, preferably involving Snowflake and AWS.
• Strong understanding of data governance, metadata management, lineage, data quality, master/reference data management, and data stewardship.
• Experience with enterprise modeling and metadata platforms such as erwin, ER/Studio, Collibra, Alation, Informatica, or equivalent technologies.
• Understanding of modern architectural practices such as data products, data mesh, data contracts, event-driven architecture, knowledge graphs, semantic layers, and API-based integration.

Leadership Competencies

• Proven ability to establish enterprise standards while maintaining productive partnerships with engineering and business teams.
• Ability to translate complex architectural issues into clear decisions and recommendations for executive audiences.
• Strong organizational influence across teams where direct reporting relationships may not exist.
• Experience developing strategies, roadmaps, operating models, and investment priorities.
• Strong talent-development capabilities, including coaching senior architects and technical leaders.
• Ability to balance strategic architecture, risk management, delivery velocity, and total cost of ownership.

Education

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, or a related field required.
• Master's degree or equivalent advanced experience preferred.
• Relevant architecture or data-management credentials, such as DAMA/CDMP, TOGAF, Snowflake, or AWS certifications, are beneficial but not required.

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