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.
We are looking for a Lead Data Warehouse Engineer to own FairMoney's data warehouse and lead the team that builds it. You will set the architecture, the modelling and quality standards, and the delivery roadmap for the data our lending, banking, risk and finance teams depend on. You will stay hands-on in architecture, design reviews and critical models while you lead and grow a team of data engineers.
1. Data Platform and Architecture
- Own the architecture of the cloud data warehouse, from ingestion to the published datasets used by analysts, BI tools and downstream systems.
- Lead the modernisation of the data platform, including the move to CDC and streaming ingestion.
- Decide which layer each piece of business logic belongs in, and keep published datasets stable for the teams that use them.
- Manage compute and storage cost and deliver measurable efficiency gains.
2. Data Modelling and Engineering Standards
- Set and enforce standards for data modelling, transformation patterns, testing, documentation and code review.
- Build data quality and observability practices that catch issues before consumers find them. ●
- Run a clear change process, with design review, QC evidence and peer review, before changes reach production.
- Drive automation and CI/CD across the data stack.
3. Reliability and Operations
- Own freshness, accuracy and availability SLAs for business-critical datasets, and the on-call and incident response behind them.
- Guide the team through complex issues in distributed data pipelines.
4. Team Leadership
- Lead, hire, coach and grow a team of data engineers.
- Set the team's working rhythm: intake and triage, sprint planning, design reviews and stakeholder reporting.
5. Stakeholder Partnership
- Be the main engineering partner to business, product, risk and finance leaders.
- Turn business requirements into reliable data products, explain trade-offs clearly and deliver on predictable timelines.
- Make sure data handling meets financial regulatory, governance and security requirements.
6. AI and Self-Serve
- Prepare the warehouse for AI use cases with documented models, consistent metric definitions and a semantic layer.
- Bring AI-assisted engineering and analytics into the team's daily work.
Must-Haves
- 10+ years in data engineering, data warehousing or analytics engineering, including 3+ years directly managing engineers.
- Expert SQL and strong Python.
- Deep data modelling experience: dimensional modelling, slowly changing dimensions, and incremental and idempotent transformations.
- Hands-on experience with a modern cloud data warehouse such as BigQuery, Snowflake or Redshift.
- Production experience with dbt and a workflow orchestrator such as Airflow.
- End-to-end ownership of business-critical datasets, with accountability for freshness, correctness and SLAs.
- Experience in fintech, banking or financial services.
- A record of hiring, retaining and growing strong engineers.
- Strong stakeholder management, including the ability to say no and explain why.
Nice-To-Haves
- Experience working with Google Cloud Platform
- Experience working with CDC or streaming ingestion (Kafka, Debezium or similar)
- Experience with data governance, data contracts and lineage
- Indepth knowledge on data quality and observability tooling
- Semantic layers and GenAI / LLM tooling on structured data
- Leading teams across time zones
Recruitment Process
- A screening call with the Recruiter for 30 minutes.
- Technical interview with the Hiring Manager for 45 minutes.