Job Description

M-KOPA’s mission is to make high quality energy affordable to everyone. OUR GROWTH SO FAR… M-KOPA has connected more than 400,000 homes in Kenya,Tanzania and Uganda to solar power with over 550 new homes being added every day.


  • Increase the velocity of our transition to a modern data stack (dbt, Airflow, Python, Spark, Synapse, Kubernetes, Docker, + ….).
  • Design and collaborate on efficient data ingestion to our data warehouse from data sources including published events, data lakes, data bases and API integrations.
  • Develop automation frameworks for data pipelines and analytics processes.
  • Set high standards of work including security, infra as code, documentation etc.
  • Integrate and maintain the infrastructure used in data analytics workstream (data lakes, data warehouses, automation frameworks).
  • Contribute to the design and implementation of a clear, concise data model.
  • Contribute to efficient storage of our data in the data warehouse, identifying performance improvements from table and query redesign.
  • Write quality ELT code with an eye towards performance and maintainability and empower other analysts to use and contribute to ELT frameworks and tools.
  • Improve the overall Data team’s workflow through knowledge sharing, proper documentation, and code review.
  • Abstract logic into libraries and patterns of work that enable teams to build value from our data independently.


  • You have a bachelors’ degree in a relevant field.
  • A minimum of 5 years professional experience in a similar field.
  • You enjoy abstracting, generalizing, and creating efficient, scalable solutions where they are needed.
  • You enjoy creating patterns and processes as well as solving presented problems.
  • Strong foundation of software development best practices (collaborative development via git, testing, continuous integration, deployment pipelines and infrastructure as code).
  • Strong SQL Skills.
  • Have experience with python.
  • Have experience deploying code to production via automated deployments on the cloud.
  • Have experience working on associated platforms (Azure, AWS etc).
  • Experience building ingestion and/or reporting from streaming datasets and event architectures.
  • Experience with distributed compute tools such as Spark and DataBricks.
  • Experience with dbt or a good basis to learn it from.
  • Experience with orchestration tools such as Airflow.
  • Familiarity with using analytics or working with analytics teams.
  • Experience with Kubernetes not expected but a plus.
  • Experience with data visualization tools such as PowerBI, Looker or Tableau
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