Data & Analytics Lead
Job Description
Flutterwave was founded on the principle that every African must be able to participate and thrive in the global economy. The Data & Analytics Lead, Group Internal Audit Function (GIAF) role is a strategic hire (a hybrid audit and data engineer) and the technical foundation of an AI-native internal audit function for our payments and banking operations: one that scales through data, automation, AI agents and continuous monitoring, with people focused on judgment, investigation and influence.
Key Responsibilities
- Design and deliver the data layer for continuous assurance, working with Engineering and Data teams to secure read-only access to production data
- Build and operate AI-powered monitoring agents across high-value use cases, including settlements and reconciliations, revenue assurance (including fee calculation and pricing), API transaction integrity, vendor spend and privileged access
- Assess the quality, completeness and lineage of priority data sources, and agree data ownership with business and technology teams
- Maintain a model and agent inventory, with documented logic, testing results, and measured accuracy and false-positive rates; ensure agents meet AI governance and responsible-AI expectations
- Partner with Engineering, Data, Finance, Treasury, Compliance, Risk, Product and IT to enable continuous assurance while preserving Audit’s independence
- Support the operating model that separates monitoring (Audit) from remediation and control ownership (management)
- Coach the audit team in analytics and help build the function’s data and AI capability over time
Key Requirements
- Bachelor’s degree in data science, computer science, engineering, statistics, finance, accounting, information systems or a related field
- Experience in internal audit, risk, fraud or revenue assurance analytics
- 7+ years of experience building data pipelines, analytics or automation in financial services, payments, fintech or a comparable data-intensive environment
- Strong SQL and Python (or equivalent), and experience with a modern data stack, such as cloud data warehouses, orchestration tools and BI or dashboard platforms
- Hands-on experience building anomaly detection, reconciliation or monitoring solutions on large transaction datasets
- Practical experience applying AI or LLM-based tools to real business problems, including testing and validation
- Strong understanding of controls, risk and assurance concepts, from audit, risk, finance or compliance experience
