Senior Analytics Engineer - Financial Inclustion at Scale at M-KOPA

M-KOPA is a fast-growing FinTech company offering millions of underbanked customers across Africa access to life-enhancing products and services. From our roots as the pioneer in pay-as-you-go �PayGo� solar energy for off-grid homes, we have grown into one of the most advanced connected asset financing platforms in the world, empowering a broad range of customers to achieve progress in their lives.

We are looking for a Senior Analytics Engineer to join our team at M-KOPA. Build the analytics infrastructure that powers data-driven decisions across our FinTech platform serving millions in Africa.

What You'll Do - You'll own analytics datasets and pipelines for specific product domains with real-world impact. Our analytics engineers have full autonomy to drive the analytics direction for their areas – from defining success metrics to building self-serve capabilities. Join us in combining cutting-edge data engineering with purpose-driven analytics.

Technical Environment 

• Modern Analytics Stack: SQL and Python in dbt on Airflow infrastructure
• Data Warehouse Architecture: Dimensional data modelling for big data infrastructures - Modern distributed data warehouse solutions (Databricks experience a plus)
• BI & Semantic Layer: Looker for semantic models and self-serve analytics
• Orchestration: Airflow for pipeline automation and scheduling

Our Analytics Approach

As a Senior Analytics Engineer, you will work as a collaborative partner. Working in a matrix organisation with 12 Analytics Engineers and 3 Platform Engineers, you'll build:

• Easy to understand and consistently modelled datasets
• Well-tested and documented data assets
• Self-serve capabilities and user coaching
• Best practices in data transformation pipelines
• Close collaboration with software engineers on data capture

What You Need

We expect you to be comfortable owning analytics infrastructure end-to-end, from defining key metrics with cross-functional partners to building production pipelines. You'll value clean dimensional modelling, embrace testing and documentation as first-order concerns, and enjoy coaching others on self-serve analytics.

Required Experience:

• Dimensional data modelling for analytics / data warehouses / big data infrastructures
• High proficiency in SQL (any dialect)
• Programming in Python, Java or other language
• Deep understanding of data warehouse technologies
• Hands-on with BI tools such as Looker or Tableau
• Track record using transformation tools such as dbt
• Practical experience with orchestration systems such as Airflow
• Strong communication skills with proven ability to explain technical concepts to non-technical audiences

Bonus Experience:

• Databricks implementation or migration experience
• Consumer-facing product analytics background

Method of Application