Data Architect

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.