About the role


Are you a data visionary who loves turning complexity into clarity? At Capitec Business Bank, we’re building the bank of the future – and that means rethinking how we enable data at scale to power decisions, AI, and client experiences. As our Lead Data Architect, you’ll help shape the blueprint that empowers our teams to self-serve, innovate, and move faster – all while keeping things lean, scalable, and trusted. If you thrive on cutting through the noise to build elegant, future-fit data ecosystems, this is your chance to lead with purpose and help us make banking simpler for millions


Minimum Experience


Bachelor’s or master’s degree in computer science, Engineering, Information Systems, Mathematics, Physics or related field.
Certifications in cloud (e.g., AWS Certified Data Analytics, AWS Certified Data Engineer, Solutions Architect), data architecture (e.g., DAMA CDMP), or TOGAF.
Understanding of AI/ML model lifecycle and its data architecture implications


You will need at least 7 years experience in the following:


Enterprise Data Strategy: Define and evolve the data architecture strategy aligned to business and AI goals (e.g., data mesh, data fabric, or data platform architecture).
Architecture Governance: Establish and enforce data standards, design patterns, and best practices for data platforms, pipelines, and models.
Solution Architecture: Design and review end-to-end data solutions across domains including operational data stores, data lakes, data warehouses, semantic layers, and feature stores.
Cloud Enablement: Architect scalable, secure, and cost-effective cloud-native or hybrid data platforms (AWS preferably or alternatively other cloud platforms).
Data Modelling: Lead enterprise-wide modelling standards – including conceptual, logical, and physical data models; support semantic modelling for self-service.
Team & Stakeholder Leadership: Mentor data engineers and architects, collaborate with cross-functional teams (AI, Analytics, product, engineering, risk, compliance), and influence data leadership forums.
Innovation & Evaluation: Assess and recommend new tools, technologies, and frameworks (e.g., Lakehouse, knowledge graphs, event-driven architecture).
Security & Compliance: Ensure data architecture adheres to governance, regulatory, privacy (e.g., POPIA, GDPR), and cybersecurity standards.


Knowledge

Minimum:

Must have detailed knowledge of:


Data Warehouse Development Life Cycle
Dimensional modelling
Financial systems and procedures
UML or equivalent modelling language
Technical Test Plan Design
IT systems development processes
Application development
Standards and governance
AI/ML Modeling Infrastructure and feature store development


Ideal:

Detailed knowledge of:


Banking systems environment
Banking business model
Agile development life cycle
Best practices for Quality Assurance (QA)


Conditions of Employment


Clear criminal and credit record