Background information about the job or company (e.g., role context, company overview)

The Co-operative Bank of Kenya Limited is incorporated in Kenya under the Company Act and is also licensed to do the business of banking under the Banking Act. The Bank was initially registered under the Co-operative Societies Act at the point of founding in 1965. This status was retained up to and until June 27th 2008 when the Banks Special General Meeti...

Responsibilities or duties

Specifically, the successful jobholder will be required to:

• 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.

Qualifications or requirements (e.g., education, skills)

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.

Experience needed

• 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.

Any other provided details (e.g., benefits, work environment, team info, or additional notes)

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• 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.

• 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.

• 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.