Architect, Data Solutions

About the role

To develop, design and maintain the solution architectures for specific business functional/technical areas within Data and Analytics. To participate in the definition of the higher-level functional and non-functional requirements, analyse technical trade-offs, determine the major components and subsystems, and define the interfaces and collaborations between them, safeguarding the strategic alignment of technology architecture with the agreed business outcomes. 1. Solution Design & Architecture Design scalable, secure, and high-performance data solutions aligned with business requirements. Define data architecture standards, patterns, and best practices. Lead the selection of appropriate technologies, platforms, and tools for data solutions. 2. Enterprise Data Strategy Contribute to the development and execution of enterprise data strategies. Align data architecture with business goals, digital transformation initiatives, and regulatory requirements. Promote data as a strategic asset across the organization. 3. Data Modeling & Integration Develop conceptual, logical, and physical data models. Architect data integration solutions across on-premises and cloud environments. Ensure data consistency, quality, and lineage across systems. 4. Cloud & Platform Architecture Design and implement cloud-native data solutions (e.g., Azure, AWS, GCP). Evaluate and integrate data platforms such as data lakes, data warehouses, and lakehouses. Optimize data storage, compute, and processing architectures. 5. Governance, Security & Compliance Embed data governance principles into solution design. Ensure compliance with data privacy regulations (e.g., POPIA, GDPR). Implement data security controls, access management, and encryption strategies. 6. Collaboration & Stakeholder Engagement Work closely with business units, data engineers, analysts, and IT teams. Translate business needs into technical requirements and data solutions. Present architectural decisions and roadmaps to senior leadership. 7. Innovation & Continuous Improvement Stay abreast of emerging technologies and trends in data architecture. Drive innovation in data engineering, analytics, and AI/ML enablement. Continuously improve architecture for performance, cost-efficiency, and agility. 8. Mentorship & Leadership Provide technical leadership and mentorship to data engineering and analytics teams. Establish architectural review processes and promote knowledge sharing. Contribute to talent development and capability building in data disciplines. Type of Qualification: First Degree Field of Study: Information Studies Other Minimum Qualifications, certifications or professional memberships Relevant Architecture Certification; AWS Certification advantageous TOGAF Frameworks Experience Required Information Lifecycle Management Data & Analytics 5-7 years Knowledge and experience working with and implementing internationally recognised frameworks; such as TOGAF, Zachman etc. Proven track record of comprehensive analysis and architectural skills to drive delivery of architectural constructs and artefacts in line with business strategy 5-7 years Proven track record of comprehensive analysis and architectural skills to drive delivery of architectural constructs and artefacts in line with business strategy Behavioural Competencies: Adopting Practical Approaches Articulating Information Challenging Ideas Checking Things Examining Information Exploring Possibilities Interacting with People Meeting Timescales Producing Output Providing Insights Taking Action Team Working Technical Competencies: Data Integrity IT Applications Knowledge Classification Knowledge Management Systems Systems Design

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