Kuda operates a neobank and lending platform serving African customers with mobile-first financial services. The tech stack spans Python, Java, and Azure/AWS infrastructure for core banking and payments, with emerging focus on data infrastructure (BigQuery, Dataflow, Kafka) and pricing optimization. Hiring is concentrated in data (5 roles) and product roles at lead level, reflecting a shift toward evidence-based pricing and credit risk management — both explicitly listed pain points.
Kuda is a fintech company founded in 2019 and headquartered in London, operating a digital banking platform for African users. The platform offers money transfers, budgeting tools, investment access, and lending via mobile devices, positioning itself as an alternative to traditional banking. The 201–500 person team operates primarily across Nigeria and South Africa. Core engineering runs on Python, Java, and .NET with Azure and AWS backing; payments and lending logic are built on ISO 8583 and credit policy systems. Recent project focus spans core banking platform modernization, payments and cards infrastructure, and pricing architecture — underpinned by new analytical infrastructure for A/B testing and credit optimization.
Primary languages: Python, Java, JavaScript, C#. Infrastructure: Azure, AWS, Azure SQL, BigQuery. Data pipelines: Kafka, Pub/Sub, Dataflow, Dataform. Testing: Cypress, Selenium, Appium. Payments: ISO 8583, SOAP, REST.
Core projects: pricing architecture design and optimization via A/B testing and analytics infrastructure; core banking platform; payments and cards platform; credit policy refinement and collections optimization; P&L forecasting and cost efficiency.
Kuda's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.