Data Engineer

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.

We are building Engineering centres of excellence across multiple regions and are looking for smart, talented, driven engineers. This is a unique opportunity to be part of the core engineering team of a fast-growing fintech poised for more rapid growth in the coming years.
To gain deeper insights into FairMoney's pivotal role in reshaping Africa's financial landscape, we invite you to watch this informative video. 

We are seeking an experienced Data Engineer with a strong background in Python programming, feature development, and expertise in AWS environments. The ideal candidate will be responsible for designing, implementing, and maintaining data pipelines and systems that support data-driven decision-making within the organization.
Responsibilities

• Data Pipeline Development: Design, construct, and maintain scalable data pipelines for efficient extraction, transformation, and loading (ETL) processes using Python and SQL.
• Feature Engineering: Collaborate with data scientists to develop and optimize features that enhance model performance and drive business insights.
• AWS Management: Utilize AWS services (such as S3, Redshift, Glue, and Lambda) to manage data storage, processing, and security effectively.
• Data Quality Assurance: Ensure data quality and integrity by implementing validation, cleansing, and error handling processes.
• Collaboration: Work closely with cross-functional teams, including data analysts, software engineers, and business stakeholders, to understand data requirements and deliver effective solutions.
• Documentation: Maintain comprehensive documentation of data pipelines, processes, and best practices to facilitate knowledge sharing.
• Performance Monitoring: Monitor data infrastructure for performance and reliability, promptly addressing any issues that arise.
• Continuous Improvement: Stay updated on industry trends and emerging technologies to continuously optimize data architecture and processes