Data Architect

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 privacysecurity 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.

Desired Background and Experience

Bachelor of Computer Science Information Systems Systems Programming or equivalent combination of relevant education and experience

Minimum 10 years of experience in data architecture information architecture or enterprise architecture.

8 years of hands-on experience in data architecture data engineering or advanced database design modeling experience in designing or implementing data warehouse/data marts MDM concepts and tools

At least 5 years of experience with data platforms such as Snowflake Databricks MongoDB and big data on cloud-based ecosystems (AWS Azure GCP). Experience working with unstructured data is a plus.

Experience working with data security privacy and regulatory requirements for handling sensitive data

Experience in designing and implementing data quality initiatives including platforms and tooling

Demonstrable experience supporting large-scale modernization or digital transformation initiatives across multiple domains and stakeholders

Ability to work across domains and stakeholders with strong communication skills

Proven ability to develop work plans for self resolve ambiguity and lead teams without authority

Industry Experience in Public sector Healthcare or Financial Services is preferable

Candidates may have previously served as Principal Architect Enterprise Information Architect Solution Architect for large enterprises or modernization initiatives

Success Measures

During the first year this role will

Establish enterprise information architecture principles.

Define reusable enterprise data products for priority domains.

Support specific modernization initiatives as a model implementation.

Introduce metadata and ownership standards.

Enable future modernization efforts to reuse common data assets rather than recreating them.

Lay the foundation for enterprise analytics and AI capabilities across the Commonwealth.