NALA

👋 About Us NALA is building Payments for the Next Billion. Faster, smarter, and fairer transfers for everyone. Since 2022, we've grown our business 120x, grown the team from 9 to 150+, raised $50M+ from top-tier investors, and were named to the Forbes Fintech 50 in 2025. We operate two core products: NALA, our consumer app makes cross-border payments cheaper, faster and more reliable for the global diaspora. Allowing users to send money from the UK, US and EU to Africa and Asia. Rafiki, our B2B payments infrastructure, is powering global payments. Our team includes alumni from Wise, Stripe, Monzo, Revolut, and CashApp — operators who’ve scaled world-class products. We act with urgency, think deeply, and put our customers first always. At NALA, this isn’t just a job. It’s ownership, impact, and the chance to change global payments forever. Join us in building Payments for the Next Billion 🙌 Your Mission As Senior Analytics Engineer, you'll own, uplift and maintain NALA's data transformation layer — the foundation that all reporting, governed metrics, self-serve analytics and AI-powered capabilities depend on. Your work will add semantic richness, structure and governance to NALA's data, ensuring every model is documented, tested and described in a way that both humans and AI agents can interpret and trust. Agentic analytics is arriving fast, and this role exists to help ensure NALA's data foundation is ready for it. 🎯 Your Responsibilities in this Role Own the transformation layer (dbt + Snowflake) — refactoring, enforcing best practices, and evolving our data stack to a best-in-class standard Take ownership of streaming data pipelines alongside batch transformation — ensuring real-time and near-real-time data flows are reliable, cost-efficient and well-integrated into the broader data architecture Establish and enforce coding & agentic coding standards, systematic testing and documentation as CI-enforced defaults across all data models Optimise warehouse performance and cost efficiency, identifying and resolving the query patterns and materialisation choices driving unnecessary spend Build the foundation for AI-powered self-serve by ensuring models carry the semantic richness and documentation that agents need to return reliable answers Scope and resolve orchestration decisions (dbt Cloud vs Dagster) and own the infrastructure roadmap for the transformation layer Support and mentor analysts on analytics engineering best practices, raising the engineering standard across the team 🔥 Must-have requirements 4+ years hands-on experience with dbt (ideally fusion) — building, refactoring and maintaining production-grade transformation layers Strong SQL, Python and data modelling skills with a clear understanding of warehousing and modern data architecture Snowflake or Databricks experience including query performance tuning and cost optimisation Deep proficiency with AI-assisted development workflows (Cursor, Windsurf, Claude Code) to force-multiply engineering output and accelerate delivery Track record of implementing testing, CI/CD, documentation standards and PR review workflows in dbt projects Comfortable owning an infrastructure roadmap — can assess the current state, propose a plan and execute without being directed step-by-step 💪 Nice to have requirements Semantic layer experience (Cube, dbt Semantic Layer) and understanding of how governed metric definitions sit on top of a transformation layer Familiarity with orchestration tools (Dagster, Airflow, dbt Cloud etc) Experience with Hex or similar modern BI/notebook platforms Experience in fintech, payments or regulated environments where data accuracy and governance carry real business consequences Familiarity with experimentation frameworks and product analytics ✅ Success in the role looks like 3-Month Metrics Full ownership of the transformation layer and warehouse, with a clear understanding of the current architecture, cost drivers and priorities 6-Month Metrics