Senior BI Engineer

Mama Money; Cape Town; On-site; ZAR 500,000 - 750,000; Full time

Job summary

Mama Money is seeking a Senior BI Engineer to enhance data structuring and reporting across the organization. In this role, you will design and build data products and models to enable self-service decision-making.

Five or more years of experience in a relevant role and strong skills in SQL, data modelling, and Python are essential. The position emphasizes collaboration with various stakeholders and the use of tools like Tableau and AWS.

Qualifications

• 5+ years’ experience in a BI Engineer, Analytics Engineer, or Data Analyst role.
• Hands‑on experience with dbt or similar transformation frameworks.
• Strong analytical ability with Python or R.

Responsibilities

• Design and build scalable data models.
• Own the development and optimisation of BI dashboards.
• Translate complex business requirements into well-defined data models.

Skills

Strong SQL skills

Data modelling principles

Python

Stakeholder management

Tools

Tableau

AWS (EKS, EC2, S3)

dbt

Job description

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 platformsWays 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