Data Engineer - Remote Eligible

Data Engineer

Kuda is a money app for Africans on a mission to make financial services accessible, affordable and rewarding for every African on the planet. We're a tribe of passionate and diverse people who dreamed of building an inclusive money app that Africans would love so it's only right that we ended up with the name 'Kuda' which means 'love' in Shona, a language spoken in the southern part of Africa. We're giving Africans around the world a better alternative to traditional finance by delivering money transfers, smart budgeting and instant access to credit through digital devices. We've raised over $90 million from some of the world's most respected institutional investors, and we're rolling out our game-changing services globally from our offices in Nigeria, South Africa, and the UK.

Role Overview

We are expanding and seeking a Data Engineer to join our ranks and champion growth. With a passion for data-driven decision-making, you will play a pivotal role in shaping the future of banking for millions.

Roles and Responsibilities

• Design, build, and maintain scalable data ingestion and processing pipelines that support analytics, machine learning, and operational use cases.
• Integrate and transform data from diverse structured and unstructured sources into trusted, high-quality datasets.
• Collaborate with business stakeholders to translate analytical and operational needs into efficient data models and pipelines.
• Monitor and optimise data pipeline performance, ensuring reliability, observability, and cost efficiency.
• Support data quality, lineage, and metadata management through governance-aligned frameworks and automation.
• Enable prescriptive and predictive analytics by preparing and curating datasets for machine learning models.
• Partner with data scientists and ML engineers to deploy and operationalise ML models into production pipelines.
• Continuously evaluate and implement modern tools and frameworks to improve data acquisition, orchestration, and delivery.
• Develop analytical datasets, metrics layers, and transformations that power dashboards, self-service analytics, and advanced insights.
• Collaborate closely with data architects, platform engineers, and business teams to ensure alignment between data strategy, architecture, and delivery.