Job Summary

The jobholder shall be responsible for leading the organization’s data engineering function, ensuring the development and maintenance of scalable, reliable, and efficient data infrastructure. The job holder shall be involved in driving the strategy, architecture, implementation and evolution of data analytics & integration platforms, tools and systems to support data-driven planning and decisioning embedded into day to day operations.

Duties and responsibilities

• Formulate and implement the organization's data engineering strategy in alignment with broader business objectives.
• Spearheads the design, development, and optimization of the organization’s data analytics infrastructure.
• Define, prioritize, and oversee data engineering initiatives to ensure alignment with strategic objectives, while optimizing allocation of tools, personnel, and budget to maximize organizational impact and ensure successful project execution.
• Evaluate and implement modern data platforms and tools, ensuring cost-efficiency, scalability, and compliance with security regulations while facilitating the technical integration of data from multiple internal and external sources.
• Oversee the design, development, and maintenance of scalable data analytics and AI platforms, capabilities, tools and processes.
• Facilitate the implementation and maintenance of robust and secure data and AI infrastructure and tools to support business data analytics needs.
• Evaluate and implement data engineering and AI platforms and tools.
• Oversee the integration of structured and unstructured data from multiple sources and using different data integration methods into and out of centralized systems.
• Monitor the performance of data systems and pipelines, identifying and resolving bottlenecks.
• Implement data observability practices to ensure the reliability and availability of data systems.
• Ensure development and maintenance of comprehensive technical documentation for data workflows and processes.
• Establish best practices for data engineering to ensure data integrity, consistency and scalability.
• Oversee implementation of corporate initiatives in the section: Ensure conformity to ISO standards and data security requirements and manage Audit, Integrity, Quality Management Systems (QMS), Risk Management programmes and staff performance.
• Promote staff career development and performance enhancement within the Section

Person specifications

For appointment to this job, the candidate must have:

• Bachelor’s degree in computer science, Information Technology, Data Science, Artificial Intelligence, Business Information Technology, Machine Learning, Mathematics, Engineering or a related field from a recognized University.
• Master’s Degree in Computer Science, Information Communication Technology, Business Information Technology, Information Security, Engineering or related field from a recognised University qualification is an added advantage.
• Must have at least one certification in data engineering or cloud native infrastructure.
• Membership to a relevant professional body.
• A Leadership Course lasting not less than four (4) weeks from a recognized institution is an added advantage.
• Cumulative service period of seven(7) years relevant work experience, three (3) of which should have been in middle level management or comparable position.

Key Competencies

• Must have implemented/upgraded and operated data analytics platforms in a comparable size of operation, playing the role of technical lead or equivalent.
• Demonstrate proficiency in data architectures, data orchestration tools and big data technologies.
• Experience in working with tools used for AI/ML data infrastructure (e.g. feature store engineering, ML Model Deployment and real-time data pipelines) is an added advantage.
• Leadership skills
• Innovative thinking
• Conflict management
• Customer-Centric Approach
• Ethical Judgment and Integrity

• Formulate and implement the organization's data engineering strategy in alignment with broader business objectives.
• Spearheads the design, development, and optimization of the organization’s data analytics infrastructure.
• Define, prioritize, and oversee data engineering initiatives to ensure alignment with strategic objectives, while optimizing allocation of tools, personnel, and budget to maximize organizational impact and ensure successful project execution.
• Evaluate and implement modern data platforms and tools, ensuring cost-efficiency, scalability, and compliance with security regulations while facilitating the technical integration of data from multiple internal and external sources.
• Oversee the design, development, and maintenance of scalable data analytics and AI platforms, capabilities, tools and processes.
• Facilitate the implementation and maintenance of robust and secure data and AI infrastructure and tools to support business data analytics needs.
• Evaluate and implement data engineering and AI platforms and tools.
• Oversee the integration of structured and unstructured data from multiple sources and using different data integration methods into and out of centralized systems.
• Monitor the performance of data systems and pipelines, identifying and resolving bottlenecks.
• Implement data observability practices to ensure the reliability and availability of data systems.
• Ensure development and maintenance of comprehensive technical documentation for data workflows and processes.
• Establish best practices for data engineering to ensure data integrity, consistency and scalability.
• Oversee implementation of corporate initiatives in the section: Ensure conformity to ISO standards and data security requirements and manage Audit, Integrity, Quality Management Systems (QMS), Risk Management programmes and staff performance.
• Promote staff career development and performance enhancement within the Section

• Leadership skills
• Innovative thinking
• Conflict management
• Customer-Centric Approach
• Ethical Judgment and Integrity
• Proficiency in data architectures, data orchestration tools and big data technologies.
• Experience in working with tools used for AI/ML data infrastructure (e.g. feature store engineering, ML Model Deployment and real-time data pipelines) is an added advantage.

• Bachelor’s degree in computer science, Information Technology, Data Science, Artificial Intelligence, Business Information Technology, Machine Learning, Mathematics, Engineering or a related field from a recognized University.
• Master’s Degree in Computer Science, Information Communication Technology, Business Information Technology, Information Security, Engineering or related field from a recognised University qualification is an added advantage.
• At least one certification in data engineering or cloud native infrastructure.
• Membership to a relevant professional body.
• A Leadership Course lasting not less than four (4) weeks from a recognized institution is an added advantage.