Head of Data

Role Overview

DigiBank & 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:

1. Data Engineering: Platform, pipelines, quality, governance, access.
2. Data Science: Dedicated services for analytics and decisioning support for D&F core services.
3. 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 Role

The 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 Outcomes

• A 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 Responsibilities

A) 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 reporting
• Product analytics and experimentation
• Portfolio, risk, and fraud analytics
• Model training and model monitoring data feeds
• Implement 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 detection
• Pricing/offer optimisation and experimentation frameworks
• Promote 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 performance
• Growth funnels and marketing effectiveness
• Financial performance and unit economics
• Operational SLAs and support metrics
• Portfolio/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 management

• Act 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 building

• Build and lead a high-performing team across:
• Data engineering
• Analytics/BI
• Data science/advanced analytics
• Set 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 & Experience

• 10+ 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).