Head of Data Analytics (Data Analyst, Cloud, Manager)

An ideal candidate should possess both technical expertise and a strong business acumen. The candidate must be well-versed in the modern data stack, which has evolved significantly over the past five years. The successful candidate will oversee the overall end-to-end data architecture for an Enterprise Data Platform. This role requires deep expertise in data architecture, cloud platforms, and emerging data technologies to drive value. The ideal candidate is deeply technical and capable of guiding the technical direction for a large organisation. Roles and ResponsibilitiesEnd-to-End Data Product Strategy: Lead the operationalization of our end-to-end data product strategy, aligning it with the company’s strategic goals. Develop an end-to-end Business Intelligence framework based on requirements, recommending appropriate architecture, analytics, and reporting solutions. Data Governance & Quality: Develop and implement a comprehensive data governance framework. Establish data quality standards and procedures to ensure accuracy, consistency, and reliability of data. Collaborate with stakeholders to address data quality issues and restore trust in the data. Data Architecture & Engineering: Provide strategic direction and leadership for data architecture and engineering initiatives. Ensure data systems and platforms are designed and implemented to support next-generation data and analytics capabilities. Mentor and guide the analytics engineering team, enhancing their skills in data architecture and engineering best practices. Team Leadership & Development: Manage and lead the Analytics and Analytics Engineering teams, fostering development and ensuring the fulfillment of the development agenda for all team members. Act as a senior advisor, offering expert guidance on complex data projects and challenges. Stakeholder Engagement: Partner with business leaders (country CEOs and HoDs) to understand their data needs and deliver actionable insights. Communicate data strategies, initiatives, and results to stakeholders clearly and effectively. Drive a data-driven culture across the organization, promoting the value of high-quality data in decision-making.