Senior BI Engineer

Job description

**Who we are:** [Mama Money](https://www.mamamoney.co.za/) is a growth-stage fintech on a mission to improve the financial lives of migrants and underserved communities across Africa and beyond. What started in 2015 as a cross-border payments service has grown into a multi-product financial platform. We give people the tools to send, save, and manage money — on their own terms, at a socially fair price. Mama HQ is in beautiful Cape Town, South Africa. We're 153 people strong (and counting), representing 19 countries across Africa, Asia, the UK, and Europe. From remittances to a growing range of financial products, everything we build is in service of people who deserve better. Our culture reflects that same commitment. We look after our team the way we want them to look after our customers. "Just be lekker!" means we trust our talented, diverse people to do what's right and make it happen — simply and with heart. We've earned a few accolades, built lasting partnerships, and grown our reach in ways we're proud of. We've had good times and tough times, but our focus has never shifted. People over profit, always. We’re looking for a Senior BI Engineer to join our Data team and become a key driver of how data is structured, surfaced, and used across Mama Money. This role goes beyond analysis — you’ll design and build the data products, models, and reporting layers that enable fast, reliable, self-service decision-making across the business. ### As our Senior Bi Engineer you will: - Design and build scalable data models (dimensional models, semantic layers, and curated datasets) that power reporting and analytics across the business. - Own the development and optimisation of BI dashboards and reporting layers, ensuring they are accurate, performant, and self-service ready. - Partner with Data Engineering to define data contracts, improve data quality, and ensure robust, well-structured pipelines. - Translate complex, ambiguous business requirements into well-defined data models and BI solutions. - Build and maintain cohort, funnel, retention, and performance datasets that enable consistent reporting across teams. - Support experimentation by ensuring A/B test data is correctly structured, tracked, and accessible for analysis. - Develop and maintain KPI definitions, metric layers, and a single source of truth for core business metrics. - Work closely with stakeholders to design dashboards that go beyond reporting — enabling real decision-making. - Perform deep-dive analysis into customer behaviour, churn, fraud patterns, and commercial performance when needed. - Champion data governance, documentation, and consistency in how data is defined and used across the organisation. - Identify opportunities to improve data architecture, reporting efficiency, and self-service capability. - Stay close to the customer journey and ensure data reflects real-world product and user behaviour accurately. ### You’ll be working with (or alongside) a stack that includes: - Cloud & infrastructure: AWS (EKS, EC2, S3), Kubernetes, Terraform - Data ingestion & processing: AWS DMS, EMR, EC2-based pipelines writing to S3 - Querying & modelling: Athena, dbt, SQL throughout (strong emphasis on modelling layers) - Reporting & BI: Tableau as primary BI tool (with focus on semantic layer and dashboard design) - Analysis & scripting: Python (Pandas, statsmodels etc) for deeper analytical work - Product & customer tooling: Zendesk, internal CRM systems, product analytics platforms - Ways of working: Agile squads, cross-functional collaboration, async documentation-first culture ### Qualifications and experience: - 5+ years’ experience in a BI Engineer, Analytics Engineer, or Data Analyst role in fintech, SaaS, or other high-volume consumer environments - Strong SQL skills with experience in building and optimising data models and transformations - Hands-on experience with dbt or similar transformation frameworks - Strong BI experience (Tableau preferred, or Power BI / Looker / Metabase) with a focus on scalable dashboarding and semantic design - Solid understanding of data modelling principles (star schema, facts/dimensions, metric consistency) - Experience supporting or enabling experimentation frameworks (A/B testing, metric tracking, data readiness) - Strong analytical ability with Python or R for deeper investigation work - Proven ability to turn complex business needs into structured, maintainable data solutions - Strong stakeholder management skills across technical and non-technical teams - Ability to balance engineering discipline + business storytelling — building trusted data products, not just reports