Data & Analytics Lead, Group Internal Audit

Flutterwave was founded on the principle that every African must be able to participate and thrive in the global economy. To achieve this objective, we have built a trusted payment infrastructure that allows consumers and businesses (African and International) make and receive payments in a convenient border-less manner. We are recruiting to fill the position below:

Job Title: Data & Analytics Lead, Group Internal Audit

Location: Lekki, Lagos 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 include but are not limited to 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. 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. 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.