Data Architect

The Data Architect is responsible for defining, governing, and evolving the enterprise data architecture to ensure that data is structured, integrated, governed, secure, reusable, and aligned with business and digital transformation objectives. The role covers current-state assessment, target-state design, enterprise data models, data domains, master and reference data, data integration, lineage, metadata, analytics platforms, and data governance alignment. Requirements • Develop and maintain the enterprise data architecture in alignment with business strategy and enterprise architecture direction • Assess the current data landscape, including data sources, data stores, applications, interfaces, ownership, duplication, and quality issues • Define target-state data architecture and transition roadmaps that support enterprise transformation initiatives • Establish enterprise data domains and define relationships between domains, business capabilities, applications, and data owners • Develop conceptual, logical, and high-level physical data models where required • Define master data, reference data, transactional data, analytical data, and metadata architecture • Identify and document authoritative data sources and Systems of Record • Design data lineage, data flow, and information exchange models across business and technology environments • Define application-to-data relationships, CRUD responsibilities, and data ownership dependencies • Design enterprise data integration and data-sharing patterns, including APIs, batch integration, event-driven exchange, and data pipelines • Support architecture for data warehouses, data lakes, lakehouses, analytics platforms, and business intelligence environments • Define data architecture principles, standards, patterns, and reference architectures • Align data architecture with data governance, data quality, metadata management, privacy, security, and retention requirements • Support Master Data Management and Reference Data Management initiatives • Collaborate with business, data governance, application, integration, security, cloud, and infrastructure teams • Participate in architecture governance and solution design reviews to ensure compliance with enterprise data architecture • Support technology and platform evaluations related to databases, data integration, analytics, metadata, MDM, and data management platforms • Identify data-related risks, gaps, dependencies, duplication, ownership issues, and improvement opportunities Required Experience • Minimum 8-10 years of experience in data management, data engineering, database design, analytics, information architecture, or related disciplines • At least 4-5 years of hands-on experience in Data Architecture or a comparable senior data design role • Demonstrated experience developing enterprise data models, data domains, lineage, and target-state data architecture • Experience with enterprise-scale transformation, data modernization, digital transformation, or data governance initiatives • Strong experience working 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 data modeling • Data domain modeling • Master and reference data architecture • Data lineage and information flow modeling • Metadata architecture and data catalog concepts • Data integration and interoperability • Data governance and data ownership alignment • Data quality architecture • Data warehouse, data lake, and lakehouse architecture • Analytics and business intelligence architecture • Database and data platform architecture • Architecture governance and assurance • Stakeholder management and consulting Preferred Frameworks, Standards & Methods • DAMA-DMBOK • TOGAF • ArchiMate • UML • Entity Relationship Modeling • Data Vault, dimensional modeling, or equivalent data modeling approaches where relevant • Data governance and metadata management practices • Cloud data architecture frameworks and well-architected principles Preferred Tools & Technology Exposure • Enterprise architecture tools such as Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, ARIS, or similar platforms • Data modeling tools such as ER/Studio, erwin Data Modeler, SAP PowerDesigner, or equivalent • Data catalog and governance platforms such as Collibra, Informatica, Microsoft Purview, Alation, or similar solutions • Relational and NoSQL database technologies • Data integration, ETL/ELT, API, and streaming technologies • Cloud data platforms across Microsoft Azure, AWS, Google Cloud, or equivalent environments • Modern analytics, data warehouse, data lake, and lakehouse technologies Preferred Qualifications & Certifications • Bachelor's degree in Computer Science, Information Systems, Data Science, Software Engineering, Computer Engineering, or a related discipline • CDMP certification is highly desirable • TOGAF or ArchiMate certification is desirable • Cloud data, database, data engineering, or data governance certifications are advantageous Personal Attributes • Strong analytical and structured thinking • Ability to understand complex enterprise data environments and translate them into clear architecture models • Strong communication, facilitation, and stakeholder management skills • Ability to engage with both business and technical stakeholders • High attention to quality, consistency, and architectural accuracy • Consulting mindset with the ability to work across multiple projects and domains Show more Show less

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

Develop and maintain the enterprise data architecture in alignment with business strategy and enterprise architecture direction. Assess the current data landscape, including data sources, data stores, applications, interfaces, ownership, duplication, and quality issues. Define target-state data architecture and transition roadmaps that support enterprise transformation initiatives. Establish enterprise data domains and define relationships between domains, business capabilities, applications, and data owners. Develop conceptual, logical, and high-level physical data models where required. Define master data, reference data, transactional data, analytical data, and metadata architecture. Identify and document authoritative data sources and Systems of Record. Design data lineage, data flow, and information exchange models across business and technology environments. Define application-to-data relationships, CRUD responsibilities, and data ownership dependencies. Design enterprise data integration and data-sharing patterns, including APIs, batch integration, event-driven exchange, and data pipelines. Support architecture for data warehouses, data lakes, lakehouses, analytics platforms, and business intelligence environments. Define data architecture principles, standards, patterns, and reference architectures. Align data architecture with data governance, data quality, metadata management, privacy, security, and retention requirements. Support Master Data Management and Reference Data Management initiatives. Collaborate with business, data governance, application, integration, security, cloud, and infrastructure teams. Participate in architecture governance and solution design reviews to ensure compliance with enterprise data architecture. Support technology and platform evaluations related to databases, data integration, analytics, metadata, MDM, and data management platforms. Identify data-related risks, gaps, dependencies, duplication, ownership issues, and improvement opportunities.