Lead Auditor, Data & Analytics (Group Internal Audit) at Flutterwave

Department: Internal Audit

About the role

• 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.
• You will design and build the data pipelines, monitoring agents and live Assurance Dashboard that let Internal Audit review every transaction, surface exceptions early and demonstrate measurable business value: revenue leakage identified, cost eliminated, losses prevented and controls strengthened.

Responsibilities
Build the platform:

• 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
• Build and maintain the live Assurance Dashboard: top risks, revenue leakage, critical findings, remediation status, fraud trends, control failures and financial exposure

Own data quality and governance:

• 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
• Ensure data protection, privacy and residency requirements are built into every pipeline and agent

Deliver measurable outcomes:

• Baseline and track outcome KPIs: revenue leakage identified, unnecessary cost eliminated, losses prevented, manual controls automated and exposure reduced
• Convert findings into quantified business value, and report progress weekly to the Head of Internal Audit and management
• Translate analytics results into clear exceptions that auditors can investigate and management can act on

Work across the business:

• 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

Required Competencies to be a Waver

• 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

Preferred:

• Exposure to payments, settlement, treasury or banking data
• Familiarity with ICFR, regulatory reporting requirements or data protection regulation
• Relevant certifications, such as CISA, CIA, CFE, or cloud and data engineering credentials

Other attributes we are looking for:

• Builder mindset: moves from idea to working, tested solution quickly
• Rigor: insists on data quality, validation and traceability
• Independence and integrity: understands the line between assurance and management responsibilities
• Communication: explains technical results to executives and non-technical colleagues in plain language
• Influence: earns cooperation from teams across the business without formal authority
• Outcome focus: measures success by business value created, not activity

How success is measured:

• Agents in production and the share of the high-risk audit universe under continuous monitoring
• Revenue leakage identified, cost eliminated and losses prevented
• Manual controls automated and manual testing hours replaced
• Accuracy and false-positive rates of agents, validated against independent testing
• Adoption of the Assurance Dashboard by management