• Architect and maintain robust, modular data models in Snowflake using dbt, following industry-standard modeling methodologies (e.g., Kimball). • Write and tune advanced SQL to ensure optimal query performance, cost-efficiency, and resource management within the Snowflake environment. • Implement and manage automated testing, monitoring, and alerting frameworks to ensure data accuracy, freshness, and lineage. • Partner with business units to define KPIs, capture requirements, and translate business logic into technical data specifications. • Own the full data lifecycle from ingestion to production-grade data marts and strategic BI visualizations and dashboard building. • Apply software engineering best practices to data development, including version control (Git), CI/CD, and detailed technical documentation. • Continuous refactoring of legacy code and data structures to improve maintainability and scalability of the analytics stack.

At Mukuru, data isn’t just numbers it’s how we unlock smarter decisions, better experiences, and greater financial access across Africa. As a Senior BI Analytics Engineer, you’ll play a key role in transforming complex data into trusted, scalable insights that power the business. Why this role matters You’ll sit at the intersection of data, technology, and business turning raw, fast-moving data into reliable, high-impact data products. Your work will directly influence decision making across a growing fintech platform serving millions. What you’ll do Build the foundation of insight - Design and develop scalable data models (facts, dimensions, data marts) in Snowflake - Translate business needs into clean, reusable, and trusted data assets - Build and optimise ELT pipelines using SQL and modern tooling Drive quality and performance - Implement data quality checks, governance, and validation processes - Continuously improve model performance, structure, and usability - Ensure data is accurate, reliable, and ready for decision-making Collaborate and influence - Work closely with Data Engineers, Analysts, and Data Scientists - Partner with stakeholders to turn requirements into impactful solutions - Contribute to Agile delivery, sprint planning, and peer reviews Shape the data ecosystem - Collaborate with architects on data modelling standards and architecture - Contribute to evolving best practices, tools, and frameworks - Mentor junior team members and support a culture of learning and excellence What you bring - 5+ years’ experience in Analytics Engineering / BI Development - Advanced SQL skills with strong experience in data transformations - Solid expertise in dimensional modelling (Kimball, star/snowflake schemas) - Experience with Snowflake, Matillion (or similar), and ELT pipelines - Understanding of data quality, governance, and testing principles - Experience working in cross-functional, Agile teams Our tech stack - Snowflake (Cloud Data Warehouse) - Matillion (ETL orchestration) - Git + Liquibase (Version control & CI/CD) - SQL (Primary language) Nice to have - Python for data automation - Exposure to Power BI or similar BI tools - Experience with data testing frameworks and observability tools - Background in fintech, consulting, or high-growth environments I am sure you are reading this job description and meet majority of the criteria BUT you may also still not be 100% comfortable in applying. We believe that there is a place for everyone under the Mukuru sun and we want YOU to contribute to our diverse tapestry of talent. So come on, take a leap of faith, and send your application if you meet majority of our requirements. Remember to include a snippet of how you will bring value and help us build a future of success that will help us determine where and how you may best be suited” Maybe you are just the future Mukurian we need!! Should you be appointed in a remote/work from home role at Mukuru, it is your responsibility to ensure that you have uninterrupted internet connectivity and a ‘work-like’ environment at your home location, in order to deliver your best in terms of performance, productivity and service to our customers. If you do not receive any response after two weeks, please consider your application unsuccessful. NB: ALL STAFF APPOINTMENTS WILL BE MADE WITH DUE CONSIDERATION OF THE COMPANY’S DIVERSITY AND INCLUSION PLANS

• Own architecting, optimizing, and transforming finance and product centric data models in DBT for flexible analysis by Data Analysts / Scientists and self-service by business stakeholders. • Design data validations to ensure data integrity throughout our pipelines, and assure the accuracy of our reporting. • Create transparency throughout the full data pipeline by establishing foundational processes with upstream data producers and downstream data consuming tools (BI, Reverse ETL, experimentation). • Advance agentic tooling and other automation efforts that help the team spend less time investigating data issues, and more time doing analyses. • Apply software engineering best practices like version control and continuous integration to the analytics code base. • Work closely with our Data Engineers, Analysts, Data Scientists, Diamond (Baseball/Softball) Sports, and Finance teams to build foundational models around new sport specific features, monthly recurring revenue, and subscription forecasting. • Support cross functional core metrics and data dependencies with leadership visibility.

• Own the Gold layer of the medallion architecture: define, version, and maintain business metrics and semantic models. • Build and maintain executive dashboards and visualizations that communicate key insights effectively. • Define and enforce best practices for data visualization, storytelling, and stakeholder adoption. • Build and maintain data marts and semantic layers that serve multiple domains across Axios. • Review and certify dashboards for accuracy, consistency, and adherence to standards. • Collaborate with data engineering to ensure Silver layer tables meet business needs. • Implement data quality tests, documentation, and lineage tracking to ensure trust in analytics outputs. • Serve as a bridge to data science, ensuring feature stores and model outputs are well-documented and reusable. • Contribute to the hub-and-spoke model: rotate through responsibilities such as intake, quality assurance, and participation in the analytics hub, while serving as a strategic partner to stakeholders, helping them shape the right questions and identify the decisions their data should inform.