There are 2 billion adults worldwide who don’t have a bank account. That’s close to 40% of the world’s adult population. 350 million of them live in Sub-Saharan Africa. They are hardworking, young, connected adults that lack access to the traditional banking system. Fido is changing that. We are empowering individuals and entrepreneurs to take advantage of opportunities in Africa.

The latest innovation from Fido called Fido Score will shift power back to the people by empowering them to build and maintain a digital financial track record.

We build consumer products powered by a magical cocktail of real-time mission-critical machine-learning models, behavioral psychology, and financial engineering. We promote meritocracy, act quickly, commit, and worship data.

Ready for your next career move?

Are you sharp, motivated, and purpose-driven? We are looking for you

Join us in our mission to make financial services accessible in Africa.

What you will do

• Build data pipelines and ETLs that collect and transform data to support ML models, analysis, and reporting
• Work in a high-volume production environment Make data standardized and reusable, from architecture to production
• Work with off-the-shelf tools including Spark, Airflow, Kafka, DynamoDb, SQS, S3, RedShift, Mysql, but often push them past their limits

Who you are

• At least 5+ years of experience in data engineering in the big data domain
• At least 3+ years of coding experience with at least one of the following: Java, Python.
• SQL expertise, working with various databases (relational and NoSQL), data warehouses, and 3rd party data sources and AWS cloud services
• Experience building and optimizing data pipelines, architecture, and data sets
• Familiarity with data engineering tech stack - ETL tools, orchestration tools, Spark, Kafka
• End to end experience - owning feature from an idea stage, through design, architecture, coding, integration and deployment stages
• Experience working with cloud services such as AWS, Azure, Google Cloud
• B.Sc. in industrial/information systems engineering, computer science, statistics, or equivalent