Data Architect

Role overview

Data architecture and delivery

• Develop enterprise data architecture aligned with business strategy and enterprise architecture; assess sources, stores, applications, interfaces, ownership, duplication, and quality.
• Define target-state architecture and transition roadmaps; establish data domains and relationships to business capabilities, applications, and data owners.
• Develop conceptual, logical, and high-level physical models; define master, reference, transactional, analytical, and metadata architecture.
• Identify authoritative sources and Systems of Record; design lineage, data flows, information exchange, application-to-data relationships, CRUD responsibilities, and ownership dependencies.
• Design data-sharing patterns using APIs, batch integration, event-driven exchange, and data pipelines.
• Support data warehouse, lake, lakehouse, analytics, and business intelligence architecture.
• Define principles, standards, patterns, and reference architectures; align with governance, quality, metadata, privacy, security, and retention requirements.
• Support MDM and reference data initiatives; collaborate across business, governance, application, integration, security, cloud, and infrastructure teams.
• Participate in architecture reviews, evaluate data platforms and technologies, and identify risks, gaps, dependencies, duplication, ownership issues, and improvements.

Required experience

• Minimum 8–10 years in data management, data engineering, database design, analytics, information architecture, or related disciplines.
• At least 4–5 years in Data Architecture or comparable senior data design work.
• Experience developing enterprise data models, data domains, lineage, and target-state architecture.
• Experience with enterprise transformation, data modernization, digital transformation, or governance initiatives, across multiple applications, databases, 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; data domains; master and reference data; lineage and information flow.
• Metadata, integration, interoperability, governance, ownership, and data quality architecture.
• Data warehouse, data lake, lakehouse, analytics, business intelligence, database, and data platform architecture.
• Architecture governance and assurance; stakeholder management and consulting.

Preferred frameworks and tools

• Preferred methods include DAMA-DMBOK, TOGAF, ArchiMate, UML, entity relationship modeling, Data Vault, dimensional modeling, governance and metadata practices, and cloud data architecture frameworks.
• Preferred tools include Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, ARIS, ER/Studio, erwin Data Modeler, SAP PowerDesigner, Collibra, Informatica, Microsoft Purview, and Alation or similar.
• Preferred technology exposure includes relational and NoSQL databases, ETL/ELT, APIs, streaming, Azure, AWS, Google Cloud, and modern analytics, warehouse, lake, and lakehouse platforms.

Preferred qualifications

• A bachelor’s degree in Computer Science, Information Systems, Data Science, Software Engineering, Computer Engineering, or a related discipline is preferred.
• CDMP certification is highly desirable; TOGAF or ArchiMate certification is desirable.
• Cloud data, database, data engineering, or data governance certifications are advantageous.

Personal attributes