Data Architect – AI Enablement

Skills, Competencies and Experience

The successful candidate will be required to have the following skills and competencies:

• A Bachelor’s degree in Computer Science, Information Systems, Data Management, Engineering, Statistics, or a related field.
  
• Professional certification in Data Architecture, Cloud Data Platforms, Data Management, or Enterprise Architecture will be an added advantage.
  
• Exposure or training in AI, machine learning, analytics, or data governance will be an added advantage.
  
• A minimum of 5 years’ relevant experience in Data Architecture, Data Engineering, Enterprise Data Management, or Solution Architecture.
  
• A strong understanding of enterprise data architecture, data modelling, integration architecture, and information management.
  
• Experience designing data solutions across operational systems, APIs, data warehouses, and analytics environments.
  
• A strong understanding of data quality, metadata, lineage, cataloguing, and governance practices.
  
• The ability to translate complex technical data concepts into clear business language.
  
• Experience working collaboratively across business, architecture, engineering, security, and governance teams.
  

• Design and govern scalable, secure, and high-quality data architectures, models, integrations, and pipelines that support AI, analytics, automation, and enterprise data initiatives.
  
• Drive data governance, quality, lineage, privacy, security, compliance, and audit readiness standards to ensure trusted and compliant AI-enabled decision-making.
  
• Assess and enhance the Bank’s data landscape by identifying architecture gaps, defining reusable data assets, and supporting AI readiness and continuous improvement.
  
• Translate business and AI requirements into practical data solutions, architecture standards, mappings, and implementation guidance.
  
• Collaborate with cross-functional teams, vendors, and stakeholders to support AI solution delivery, architecture reviews, innovation initiatives, and enterprise data best practices.