Data Architect

Role overview

Architecture and delivery responsibilities

• Develop and maintain enterprise data architecture, principles, standards, patterns, and reference architectures.
• Assess current data sources, stores, applications, interfaces, ownership, duplication, and quality issues; define target-state architecture and transition roadmaps.
• Establish data domains and relationships to business capabilities, applications, and owners; develop conceptual, logical, and high-level physical models.
• Define architecture for master, reference, transactional, analytical data, metadata, authoritative sources, and systems of record.
• Design data lineage, flows, information exchange, application-to-data relationships, CRUD responsibilities, and ownership dependencies.
• Design integration and data-sharing patterns, including APIs, batch, event-driven exchange, and data pipelines.
• Support architecture for warehouses, lakes, lakehouses, analytics, and business intelligence platforms.
• Align architecture with governance, quality, metadata, privacy, security, and retention; support MDM and reference data initiatives.
• Collaborate with business, governance, application, integration, security, cloud, and infrastructure teams; participate in reviews and platform evaluations.
• Identify data risks, gaps, dependencies, duplication, ownership issues, and improvement opportunities.

Experience requirements

• 8–10 years in data management, data engineering, database design, analytics, information architecture, or a related discipline.
• At least 4–5 years of hands-on data architecture or comparable senior data design experience.
• Demonstrated enterprise data modeling, data domain, lineage, and target-state architecture experience.
• Experience with enterprise-scale transformation, data modernization, digital transformation, or data governance initiatives.
• Experience across multiple applications, databases, data platforms, and integration technologies.
• Experience working with business stakeholders, data owners, data stewards, technology teams, and senior management.

Core competencies

• Enterprise data architecture, conceptual and logical modeling, and data domain modeling.
• Master and reference data architecture, lineage, information flow, metadata, and data governance.
• Data integration, interoperability, data quality, and ownership alignment.
• Data warehouse, lake, lakehouse, analytics, business intelligence, database, and data platform architecture.
• Architecture governance, assurance, stakeholder management, and consulting.

Preferred frameworks and qualifications

• DAMA-DMBOK, TOGAF, ArchiMate, UML, entity relationship modeling, Data Vault, dimensional modeling, or equivalent approaches are preferred.
• A bachelor’s degree in a related discipline is preferred.
• CDMP is highly desirable; TOGAF or ArchiMate certification is desirable. Cloud data, database, data engineering, or data governance certifications are advantageous.

Preferred tools and technology exposure

• Enterprise architecture tools such as Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, or ARIS; data modeling tools such as ER/Studio, erwin Data Modeler, or SAP PowerDesigner.
• Data catalog and governance platforms such as Collibra, Informatica, Microsoft Purview, or Alation.
• Relational and NoSQL databases; data integration, ETL/ELT, API, and streaming technologies.
• Cloud platforms including Azure, AWS, and Google Cloud, and modern analytics, warehouse, lake, and lakehouse technologies.

Personal attributes