Senior BI / Data Analyst

Who we are:

Mama Money  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 / Data Analyst to turn Mama Money’s data into answers the whole business can trust. You’ll sit where analysis meets BI engineering — digging into the numbers to understand how our customers behave and how the business is performing, and building the models and dashboards that let everyone else find those answers for themselves.

This is a role for someone who loves the analysis and the craft behind it in equal measure. You’re as happy investigating why a metric moved as you are shaping the semantic layer so the same number means the same thing everywhere it appears. If you like turning messy questions into clear, reliable answers — and building things once so they scale — you’ll feel right at home.

As our Senior Bi / Data Analyst you will:

• Partner with teams across Mama Money to answer their most important questions — from customer behaviour, churn, and retention to fraud patterns and commercial performance — and turn what you find into clear, actionable insight.
• Design and build scalable, dimensional data models and semantic layers that serve as the single source of truth for reporting and analytics.
• Build, optimise, and maintain Tableau dashboards that people actually use — designed for self-service, performance, and reuse rather than one-off requests.
• Define and maintain KPI and metric definitions so the same number means the same thing everywhere it appears.
• Build and maintain cohort, funnel, retention, and performance datasets that power consistent reporting across teams.
• Structure and track A/B test data so experimentation results are reliable and easy to analyse.
  Translate ambiguous, complex business questions into well-structured data models and BI solutions stakeholders can rely on.
• Partner closely with Data Engineering to strengthen data quality and make sure the pipelines feeding your work are robust and well-structured.
• Champion data governance and documentation, keeping metric and dimension definitions consistent across the business.
• Spot and drive improvements to how we model data and report on it, raising the bar for reporting efficiency across the team.

Qualifications and Experience:

• 5+ years in a BI, analytics, or data analyst role in fintech, SaaS, or another high-volume consumer environment.
• Strong, demonstrable SQL — you’re comfortable building and optimising data models and transformations, and this is where a lot of the role lives.
• Excellent BI and dashboarding skills (Tableau preferred; Power BI, Looker, or Metabase very welcome), with a track record of dashboards and semantic layers that scale.
• A solid grounding in data modelling principles — star schemas, facts and dimensions, and keeping metrics consistent at scale.
• Hands-on experience with dbt or a similar transformation framework
  Experience getting data experiment-ready — A/B testing, metric tracking, and making results reliable to analyse.
• Comfortable reaching for Python or R when a question goes beyond the dashboard.
• A track record of turning ambiguous business asks into structured, maintainable data products.
• Strong stakeholder skills across technical and non-technical teams, with the judgement to point people to a better-modelled answer when one already exists.
• The blend of analytical rigour and storytelling it takes to build data products people genuinely trust — and act on.

In your first 90 days:

• In your first 90 days, you'll get to know Mama Money's data stack, models, and stakeholders, then start taking ownership of existing dashboards and reporting to find quick wins and inconsistencies worth fixing.
• By the end of the quarter, you'll have shipped a meaningful improvement to our data models or semantic layer, complete with clear metric definitions, and led at least one cross-functional question — like churn, retention, or fraud patterns — from a messy ask through to a trusted, self-service answer.