Mama Money - Senior BI Engineer
Requirements
• 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
Responsibilities
• 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
Benefits
• At Mama Money, we’re here to make it easy—for our customers, our communities, and for each other. Simplicity is at the heart of what we do, whether it's creating a seamless and stress-free customer journey or removing barriers that stand in the way of progress, our work is all about delivering impact, minimizing complexity, and ensuring every experience with Mama Money is easy. • We’re a team that isn’t afraid to give it a go. We value taking action, making bold steps, embracing innovation, solving problems, doing the hard things, and thinking smartly. Mistakes and failures don’t stop us—they teach us. Every challenge is a chance to grow, do better, and push boundaries. • And we just own it. Whether it’s delivering results, collaborating across teams, driving urgency, or taking accountability for our actions, we approach every task with integrity and purpose. We make company goals our own, showing up for each other, our customers, and our mission to create lasting change. • These aren’t just words—they’re how we show up every day: • Make it easy for customers • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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