👋 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.
Orchestration. You have scheduled, monitored, and repaired a pipeline in Airflow, Dagster, or an equivalent.
CI/CD and deployment management. You have run a project where a pull request builds the changed models and their downstream dependencies against production state, writes them to an isolated schema or clone, and promotes that same selection after merge. You can describe the path from a developer environment through CI into production, how a bad deploy is stopped or rolled back, and the signal that a deploy succeeded.
Three or more years of production Python. You have shipped Python in a pipeline: transforms and checks that run without you. A take-home test, or a notebook, is not this requirement.
You are passionate about building with agents and for agents. Serving agents data at scale is a problem you want. You have used frontier tooling to automate a traditional analytics workload, such as a recurring model change, a reconciliation, or a report. You can show the outcome in time or volume. You can explain how that system is observed, how it is governed, and how it scales past one person on a laptop.

💪 Nice to have requirements

An ontology, or a business entity model, on top of a semantic layer.
A distributed engine such as Spark or Trino, used on data that did not fit a single warehouse query.
Fintech, payments, or another regulated environment.

✅ Success in the role looks like

3-Month Metrics

You own the transformation layer and can name the models people do not trust, the numbers that disagree, and the queries that cost money.
A change to a live model is agreed and visible before it lands.
A teammate can follow your reasoning without a long private explanation.
You have one automation a teammate can run, with a before and after in time or volume.

6-Month Metrics

Keep cost, monitoring, and streaming from the current advert.
Replace "autonomous agents with measurable before and after" with: the team uses the automation you taught them, and you can show the hours or the volume it changed.

➡️ Interview Process

Before you apply, take a look at our Extra Preparation guide, covering our engineering philosophy, tech stack, and what to expect at each interview stage.

You will need to first submit your application through our ATS Workable. There is no need to submit a Cover Letter.

If successful you will be selected for our interview process, which has the following stages:

[30mins] Interview with the Talent Team

We want to get to know you and follow up on your experience and motivations. The best preparation is to really know why you applied for the role, including your application questions.

[45mins] Interview with the Hiring Manager

You'll meet Parth, who leads our Data team. This goes deep on how you have used dbt to solve business problems, how you think about modelling, and how you actually work with AI. The best preparation is to know what's on your CV really well.

Take-Home Task

A real-world task, scoped at around 2 to 3 hours, with a week to return it. Full use of AI tooling is encouraged, and we ask you to share your prompts alongside the work. It is how we build, and we would rather see it than pretend otherwise.

[1hr] Technical Deep Dive with the Data Team

You present your take-home to two members of the data team and field questions on your architecture and decisions.

[45mins] Bar Raiser

A final conversation with a member of our leadership team about track record, ambition and motivations, and your chance to ask whatever you still want to know about the business.

⭐️ Benefits

Private Medical Insurance: Full medical cover through a leading UK provider, because your health comes first.
27 Days Off Plus UK Bank Holidays: Take the time to decompress. Working at a startup is hard!
Birthday Leave: Celebrate your special day with a bonus day off to take in that month.
Enhanced Parental Leave: We offer 16 weeks of full pay for the primary caregiver and 4 weeks of full pay for the secondary caregiver (after a 6-month probationary period).
Enhanced Pension: Salary sacrifice pension scheme via Penfold, giving you flexibility and control over how you save for your future.
Life Insurance: 3x salary cover, giving you and your family peace of mind.
Global Workspace: Get access to WeWork locations worldwide.
Hybrid Working: We work in our Canary Wharf Office four days a week (Monday, Wednesday, Thursday and Friday).
Learning Budget: Fuel your growth with $1000 annually for learning and development.
Sarabi: Themed snacks and Friday lunch focused on building great working relationships with the team.
Monthly Socials: Join fun social events every month for great times.
Free Coffee: Enjoy barista-style coffee at your fingertips.

Senior Analytics Engineer