Jobs at AXIAN Group

Role OverviewDigiBank & FinTech (D&F) is building a multi-country digital banking and fintech platform across African markets. Bridging telecom, mobile money, and banking services to accelerate inclusion and scale sustainable growth. In this environment, data is a strategic asset and an operational dependency: it powers customer, product and market insights, portfolio and risk decisioning, performance management, product optimisation, and regulatory-grade reporting.The Head of Data is accountable for D&F’s end-to-end data strategy and execution across three core verticals:Data Engineering: Platform, pipelines, quality, governance, access.Data Science: Dedicated services for analytics and decisioning support for D&F core services.BI & Reporting: Single source of truth, dashboards, performance cadence, self-service analytics.This is a centralised group function that maintains dedicated D&F data infrastructure, builds a durable analytical capability, and ensures consistent reporting and metrics across products, markets, and functions.Purpose of the RoleThe Head of Data needs to establish and run a trusted, secure, scalable, and business-enabling data ecosystem for D&F. Turning multi-market operational data into decision advantage. The Head of Data ensures that executives and teams can move faster with confidence: reliable data foundations, actionable insights, and measurable impact, delivered with disciplined governance and compliance.Key OutcomesA resilient data platform that supports reporting and decisioning workloads with high availability, clear ownership, and strong cost discipline.“Single source of truth” metrics and dashboards that underpin daily/weekly/monthly operating cadences, executive reporting, and market performance management.A high-performing data science and analytics capability delivering measurable uplift in growth, retention, product oversight and performance outcomes, risk detection, and operational efficiency. Strong data governance: privacy, access control, lineage, auditability, data quality, and cross-market consistency, aligned to regulatory expectations.A culture of accountability where teams rely on shared definitions, controlled experimentation, and transparent measurement.Core ResponsibilitiesA) Data strategy, operating model, and governance (cross-vertical)Define and execute the D&F data strategy aligned to the cluster’s business objectives, multi-market operating model, and product roadmap.Establish a clear data operating model: roles, responsibilities, prioritisation, delivery cadence, and service catalogue (platform services, analytics services, reporting services).Own enterprise-grade data governance for D&F, including:Data classification, access management, encryption standards, audit trails, retention policies, and secure sharing.Metric governance and a controlled KPI framework (definitions, owners, changes, approvals).Data quality management (tests, monitors, SLAs, incident response).Partner with Risk, Compliance, Legal, Security, and Technology to ensure regulatory-grade data controls and a defensible audit posture across jurisdictions.B) Vertical 1: Data Engineering (platform, pipelines, reliability)Own the design and evolution of the D&F data architecture (data lake and warehouse patterns as appropriate), ensuring scalability across countries, products, and data domains.Lead the build and operation of ETL/ELT pipelines (batch and event-driven where relevant), supporting:Executive and operational reportingProduct analytics and experimentationPortfolio, risk, and fraud analyticsModel training and model monitoring data feedsImplement best practices in observability and reliability: logging, lineage, versioning, replay capability, incident triage, and post-mortems.Ensure strong data security and access control across markets and teams, with appropriate anonymisation/pseudonymisation where required.Drive platform automation and infrastructure reproducibility via IaC and disciplined environment management, balancing cost, performance, and governance.C) Vertical 2: Data Science (advanced analytics, ML enablement, responsible AI)Build and lead a data science capability that delivers measurable business impact, including (as applicable):Propensity and lifecycle models (activation, retention, cross-sell)Credit analytics (risk segmentation, affordability proxies, early warning indicators)Fraud and anomaly detectionPricing/offer optimisation and experimentation frameworksPromote ethical and responsible analytics: interpretability, bias testing, drift detection, and appropriate governance, especially for automated decisions impacting customers.Translate complex insights into executive-ready decisions through clear framing, quantified impact, and operational handover plans.D) Vertical 3: BI & Reporting (dashboards, performance cadence, single truth)Own the D&F reporting strategy and execution, ensuring consistent, trusted, and timely insights across:Product performanceGrowth funnels and marketing effectivenessFinancial performance and unit economicsOperational SLAs and support metricsPortfolio/credit/fraud performance (in partnership with Risk/Portfolio/Product functions)Build a scalable self-service BI layer with strong semantic models, controlled definitions, and role-based access. Reducing dependency and accelerating decision-making.Run a disciplined executive reporting cadence (weekly ops, monthly performance, quarterly planning support), including insight narratives and “so-what” recommendations.E) Stakeholder leadership and delivery managementAct as the single accountable leader for D&F’s data roadmap and prioritisation, balancing multi-market needs and platform sustainability. Partner closely with Product, Growth, Marketing, Finance, Compliance, DigiBank, and Markets to convert business problems into data initiatives with clear delivery milestones and impact metrics.Establish cross-functional ways of working: intake process, quarterly planning, sprint/kanban execution where appropriate, and transparent reporting of progress and outcomes.F) Team leadership and capability buildingBuild and lead a high-performing team across:Data engineeringAnalytics/BIData science/advanced analyticsSet clear role expectations, coaching, technical standards, and performance management; develop a pipeline of talent across markets.Create a culture of craft, accountability, and continuous improvement (automation, reuse, quality-by-design).Required Skills & Experience10+ years in data leadership roles spanning data engineering + analytics/BI, with demonstrated delivery at scale (preferably multi-market or multi-business-unit).Proven track record building trusted data foundations (architecture, pipelines, governance, security, quality) and delivering measurable business outcomes.Strong understanding of analytics and ML delivery in production environments, including monitoring and lifecycle management (in partnership with engineering).Executive-level stakeholder management: ability to influence roadmaps, drive prioritisation, and communicate trade-offs clearly.Strong command of data governance, privacy, and audit requirements in regulated environments.Experience in fintech, mobile money, lending, telecom data, or financial services ecosystems in Africa.English required; French advantageous (multi-market stakeholder engagement). 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