Lead Auditor, Data & Analytics (Group Internal Audit)

About this role

The role is for a Lead Auditor in Data & Analytics within Flutterwave's Group Internal Audit. It combines audit and data engineering to build an AI-native internal audit function for payments and banking operations. The position involves creating data pipelines, monitoring agents, and a live Assurance Dashboard to surface exceptions, track risks, and demonstrate business value such as identifying revenue leakage and preventing losses. It requires collaborating with multiple business and technical teams to enable continuous assurance while preserving audit independence. The job targets candidates with extensive experience in data pipelines, analytics, and assurance in financial services or fintech environments.

What you would do

• Design and deliver the data layer for continuous assurance with read-only production data access
• Build and operate AI-powered monitoring agents across high-value use cases
• Maintain the live Assurance Dashboard showing risks, findings, and remediation status
• Own data quality, governance, and data lineage with defined ownership
• Maintain a model and agent inventory with validated logic and testing results
• Ensure data protection, privacy and residency requirements in pipelines and agents
• Deliver measurable outcomes and report progress to Head of Internal Audit and management
• Translate analytics results into actionable exceptions for auditors and management
• Collaborate with Engineering, Data, Finance, Treasury, Compliance, Risk, Product and IT to enable continuous assurance
• Coach the audit team in analytics and help grow the function's data and AI capability over time

What they are asking for

• Bachelor's Degree in a relevant field (data science, computer science, engineering, statistics, finance, accounting, information systems) or related
• 7+ years in building data pipelines, analytics or automation in financial services, payments, fintech or similar environment
• Strong SQL and Python skills and experience with a modern data stack (cloud data warehouses, orchestration tools, BI/dashboard platforms)
• Hands-on experience building anomaly detection, reconciliation or monitoring on large transaction datasets
• Practical experience applying AI or LLM-based tools to business problems with testing and validation
• Solid understanding of controls, risk and assurance concepts from audit, risk, finance or compliance
• Preferred: exposure to payments, settlement, treasury or banking data; familiarity with ICFR, regulatory reporting or data protection
• Relevant certifications (eg CISA, CIA, CFE) or data engineering/cloud credentials
• Other attributes: builder mindset, rigor, independence, communication, influence, outcome focus

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