FairMoney is a pioneering mobile banking institution specializing in extending credit to emerging markets. Established in 2017, the company currently operates primarily within Nigeria, and it has secured nearly €50 million in funding from renowned global investors, including Tiger Global, DST, and Flourish Ventures. FairMoney maintains a strong international presence, with offices in several countries, including France, Nigeria, Germany, Latvia, the UK, Türkiye, and India.
In alignment with its vision, FairMoney is actively constructing the foremost mobile banking platform and point-of-sale (POS) solution tailored for emerging markets.

The journey began with the introduction of a digital microcredit application exclusively available on Android and iOS devices. Today, FairMoney has significantly expanded its range of services, encompassing a comprehensive suite of financial products, such as current accounts, savings accounts, debit cards, and state-of-the-art POS solutions designed to meet the needs of both merchants and agents.

To gain deeper insights into FairMoney's pivotal role in reshaping Africa's financial landscape, we invite you to watch this informative video.

Role and Responsibilities

As a Data Engineer at FairMoney, You will be responsible for mainly, but not limited to:

• Work closely with Data Analysts/Scientists, understand the business problems, and translate the requirements into a database, ETL, or reporting solution
• Design, build, and maintain ingestion and integration of multiple types of data sources
• Proficiency in converting heterogeneous data sources to simplified Data Models following Data Warehousing Best Practices for reducing the time-to-value of data for analysis
• Build data orchestration pipelines over Airflow for automation and scheduling
• Implement tools and processes for ensuring data quality, and freshness with reliable, versioned, and scalable solutions
• Identify issues in data flow and improvements in data stack which comprises visualization tools (Tableau/Power BI), data warehouse (Snowflake), modeling (dbt), git, and other in-house built as well as open source tools.
• Implementing new technologies in a production environment to create a frictionless platform for data analytics and science teams
• Make it easy for business stakeholders to get a better understanding of data, and make the organization data-literate for self-serve analytics.