Senior Analytics Engineer (Hiring Immediately)

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 & 2026.

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 for global giants like MoneyGram & Western Union.

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

We are refactoring the warehouse for agents and cutting its cost.

Analysts, operators, executives, and agents depend on the warehouse. The same question has to return one answer.

As Senior Analytics Engineer, you own the transformation layer in dbt and Snowflake. You make it dependable: well modelled, tested, documented, and clear enough that a person or an agent can trust an answer. You are the data-engineering bar for the team, and you work alongside our other analytics engineer.

You get up to speed on the stack we have, then help build the post-AI data stack: artifacts an agent can read, tools an agent can operate, context we own, and one reality the company can check.

You set the quality standard by example. You are strong enough to shape the data stack over the long run.

Your Responsibilities in this Role

• Own the modelling and transformation of your domains end to end in dbt and Snowflake, from the business question through to the governed model that answers it
• Set and defend modelling methodology alongside our existing Analytics Engineer, grain, dimensional patterns, and where the exceptions genuinely earn their place
• Make your models agent-ready: semantically rich, consistently named, described in business terms, and safe for something to query without a person checking the output
• Own streaming and near-real-time pipelines (Kafka or similar) alongside batch transformation, keeping real-time flows reliable and cost-efficient before they become a bottleneck
• Establish and enforce coding and agentic-coding standards, systematic testing and documentation as CI-enforced defaults
• Optimise warehouse performance and cost, finding and fixing the query patterns and materialisation choices driving unnecessary spend
• Deploy and supervise autonomous agents against our data stack, and hold our data infrastructure to proper engineering standards: monitored, wired to incident.io and PagerDuty, with escalations, SLAs and documented processes
• Support and mentor analysts on analytics-engineering practice, raising the engineering standard across the team

Must-have requirements

• Three or more years of production dbt. You can defend a modelling methodology. Kimball sits inside that defence: grain, facts, dimensions, and where a wide table is the wrong choice.
• A semantic layer you have used in production. You can explain metrics, dimensions, grain, and joins, and how an agent reads them.
• Streaming and warehouse scale. You have owned CDC or another stream, incremental transforms, and a freshness choice with a cost. You can say when a batch model is the wrong place for a high-frequency number, and what sits beside the warehouse: a stream, a low-latency table, or a semantic API.
• Snowflake or Databricks. You have worked on cost and scale: incremental versus full refresh, clustering, and a lag target a consumer needs.