About the job
About Us
NALA is revolutionizing global payments for the next billion people, providing faster, smarter, and fairer transfer solutions for everyone. Since our inception in 2022, we have experienced an extraordinary growth of 120x, expanded our team from 9 to over 150 professionals, secured $50M+ in funding from leading investors, and earned a spot in the prestigious Forbes Fintech 50 in 2025.
We deliver two key products:
- NALA, our consumer app, enables affordable, quick, and reliable cross-border payments for the global diaspora, facilitating money transfers from the UK, US, and EU to Africa and Asia.
- Rafiki, our B2B payments infrastructure, powers seamless global transactions.
Our dynamic team comprises alumni from renowned companies like Wise, Stripe, Monzo, Revolut, and CashApp—experts who have successfully scaled world-class products. We embody a culture of urgency, deep thinking, and unwavering customer focus.
At NALA, this is more than just a job; it’s about taking ownership, making an impact, and redefining global payments for the better.
Join us in our mission to create Payments for the Next Billion.
Your Mission
As a Senior Analytics Engineer, you will take charge of enhancing and maintaining NALA's data transformation layer—the backbone for all reporting, governed metrics, self-service analytics, and AI-driven functionalities. Your contributions will enrich the semantic context, structure, and governance of our data, ensuring that every model is comprehensively documented, tested, and interpretable by both humans and AI systems. With the rise of agentic analytics, your role is crucial in ensuring that NALA's data infrastructure is adequately prepared.
Your Responsibilities in this Role
- Oversee the transformation layer (dbt + Snowflake), implementing best practices and advancing our data stack to achieve excellence.
- Manage streaming data pipelines alongside batch transformations, guaranteeing reliable and cost-effective real-time data flows integrated into the overall data architecture.
- Establish and uphold coding and agentic coding standards, ensuring systematic testing and documentation are default practices across all data models.
- Optimize warehouse performance and cost efficiency by identifying and rectifying query patterns and materialization decisions leading to unnecessary expenditures.
- Lay the groundwork for AI-enhanced self-service capabilities by developing robust data models and interfaces.

