Hiring Enterprise Data Architect

The Enterprise Data Architect is responsible for defining and evolving the Commonwealth’s enterprise information architecture and reusable data products to support transformation and AI priorities. 

This role partners with business leaders, product and engineering teams and enterprise technology org to identify which information assets should be shared across agencies and which should remain domain specific. The position will define data domains, create data models, ensure data quality, map source to domain and assist in creating contracts to support modernization initiatives and future AI capabilities. The role serves as the bridge between business processes and technical implementation, ensuring that new solutions contribute to a sustainable enterprise data foundation rather than creating additional silos. The ideal candidate possesses a comprehensive understanding of data architecture principles, practical data modeling experience, and deep expertise in data-centric architecture.

Key Responsibilities

1. Enterprise Information Architecture

• Define conceptual and logical data models for common business entities.

• Establish canonical models and enterprise information standards.

• Define relationships between enterprise and domain-specific data assets.

• Design enterprise information architecture that incorporates data classification, privacy, security, and regulatory requirements by design.

2. Modernization Programs

• Partner with workflow modernization teams (product and engineering) to embed reusable data products into solution designs.

• Analyze legacy systems and identify information assets suitable for enterprise reuse.

• Prevent duplication of data structures across applications and agencies.

• Support data migration strategies and target-state architecture.

3. Data Product Architecture

• Identify and support design of reusable data products

• Define schemas, interfaces, metadata and quality requirements

• Establish product boundaries, stewardship and ownership models.

4. Data Governance and Metadata

• Establish data architecture principles, data standards, best practices, and guidelines including data catalog, data lineage, observability, security and interoperability

• Define business definitions and data quality expectations.

5. Platform and Technology Collaboration

• Work with engineering and platform teams to translate business concepts into technical designs.

• Partner with cloud, integration and data teams to implement enterprise data products.

• Promote API-first and product-based approaches to information sharing.

6. AI Enablement

• Ensure enterprise data products are discoverable, governed and suitable for future AI use cases.

• Support semantic layers, knowledge graphs and natural language access to enterprise information.