Head of BI & Analytics

Kuda Technologies

23h•Hybrid

About The Position

Kuda is looking for a Head of BI & Analytics to lead our BI & Analytics function, delivering the trusted reporting and product insight that drive decisions across the business. You will lead a BI Engineering team scaling our semantic layer as the single, tested source of truth, and an Analytics team embedded in product squads driving deeper product insight and more advanced analytics. You will lead adoption of our next-generation BI platform, expand self-serve and agentic AI use across the organisation, and set the strategy for how AI changes BI & Analytics work. You will work closely with the Heads of Product, CX, Marketing and Data Engineering.

Requirements

Proven experience leading data teams of managers, engineers and analysts, including developing people and managing performance.
Strong SQL, with enough hands-on depth to review the team's work and set the technical standard.
Strong understanding of data development practices, including how tables and models are built, tested, versioned and deployed into a data warehouse.
Experience designing or scaling a semantic layer or governed metrics layer, and defining KPIs that the business trusts.
Strong forecasting and modelling experience, including models such as lifetime value, revenue or unit economics that support commercial and product decisions.
Experience shaping product strategy with data, for example through pricing, proposition or tier design, and influencing senior decision-makers.
Track record of driving adoption of BI tools and data products among non-technical teams, including through change management and influencing without authority.
Ability to partner with senior stakeholders, challenge priorities constructively and communicate clearly to technical and non-technical audiences.
Clear perspective on how AI is changing BI & Analytics, and the ability to turn it into a practical, responsible plan.

Nice To Haves

Experience with BigQuery, Looker and Dataform (or dbt).
Experience in fintech, banking or other financial services.
Hands-on experience with AI-assisted analytics or agentic AI in a BI context.
Experience with experimentation and advanced analytics methods in a product setting.
Knowledge of African markets.
Bachelor's degree in CS, Statistics, Business, Mathematics, Economics, or a related field.

Responsibilities

Lead and develop a team spanning BI Engineering, pricing and product analytics, setting clear goals and raising the quality bar.
Own the BI strategy and roadmap, including implementing our semantic layer in our new BI platform and extending governed, tested gold-layer domains as the single source of truth for core reporting.
Drive adoption of the BI platform and agentic AI across Kuda, leading change management with product, CX, marketing and operations teams so trusted, self-serve data is used in everyday decisions.
Own core business reporting and KPI definitions, ensuring accuracy, consistency and clear ownership across functions.
Support the design and tracking of company and team OKRs.
Develop the Analytics team as it deepens engagement with product squads: shaping roadmaps, experimentation, and the move from descriptive reporting to more advanced analytics.
Help shape product and commercial strategy by building the analytical cases for decision making, and by partnering with Product and Finance to quantify trade-offs and expected impact.
Support/ own forecasting and financial modelling, building models such as customer lifetime value, revenue and unit economics that inform planning and leadership decisions.
Define Kuda's strategy for how AI will change BI & Analytics, from AI-assisted analysis and development to governed semantic models that AI agents can query reliably, and lead delivery within data privacy guardrails.
Work closely with Data Engineering on data landing in our warehouse staging layer, agreeing standards for modelling, testing, deployment and release so new data products reach production quickly and reliably.
Uphold and improve development best practices: version control, code review, documentation, data testing and monitoring, and retirement of reports and dashboards that are no longer used.
Partner with the Heads of Product, CX, Marketing and Data Engineering to turn business questions into clear priorities, and present findings and recommendations to senior leadership.