Executive: Data Management and Analytics

SUMMARY:
Are you a visionary data leader ready to build and scale an enterprise-wide data ecosystem? This is your opportunity to step into a leadership role where data strategy meets business impact.

POSITION INFO:
Our client – a leading cell captive insurance innovator – is seeking an Executive: Data Management and Analytics to establish and lead their Data Centre of Excellence, shaping the organisation’s data governance, architecture, and analytics capabilities. You’ll be at the helm of driving a data-first culture, and empowering actuarial, finance, risk, and compliance teams with trusted insights that inform strategic decisions.

If you thrive at the intersection of data, technology, and business strategy, this is the kind of role that defines careers.

Key Responsibilities:

• Drive the implementation of the enterprise data and analytics strategy, ensuring measurable business value.
• Champion data governance, architecture, and data quality across the organisation.
• Lead cross-functional alignment on data standards, metadata, and KPIs to create enterprise-wide consistency.
• Build, coach, and inspire a high-performing data team to deliver excellence across business functions.

• Education: Bachelor’s Degree in Data Science, Statistics, Actuarial Science, Mathematics, Computer Science, or a related field (Master’s preferred).
• Experience: Minimum of 8 years’ experience in data, analytics, or technology roles (preferably in insurance, banking, or telecoms), with at least 3 years in a leadership capacity. Proven success in delivering enterprise-wide data and analytics projects.
• Skills: Strong proficiency in Azure Synapse, SQL, data integration, metadata and master data management. Deep knowledge of South African data governance and compliance.
• Other: Demonstrated success in bridging business and technology and driving adoption of enterprise data solutions.

• Actuarial roles (Life, Short-Term, Health, Pensions, Quantitative)
• Data Scientists / Data Analysts (Python, R, SQL, Machine Learning)
• Risk Analysts (Credit, Market, Model Risk, Operational)
• Pricing Specialists (Insurance, Financial Products)
• Machine Learning & AI Data Scientists (ML Ops, NLP, Predictive Modelling)
• Quantitative Specialists across Banking, Insurance, and FinTech

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