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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
This is a foundational engineering hire for our Data team, dedicated to data foundations and infrastructure, the layer that everything else at NALA depends on. It directly underpins our number-one company priority, the US growth initiative, and sits across the whole data roadmap.
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. You'll add semantic richness, structure and governance to our 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 make sure 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 (Kafka or similar) alongside batch transformation, making sure real-time and near-real-time flows are reliable, cost-efficient and well-integrated into the broader data architecture before they become a bottleneck
- Establish and enforce coding and 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
- Deploy and supervise autonomous agents against the data stack, and move our data infrastructure onto proper engineering standards: monitored, wired to incident.io and PagerDuty with escalations and SLAs, and the supporting processes documented
- 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
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Must-have requirements
- 4+ years hands-on experience with dbt (ideally fusion), expert-level, building, refactoring and maintaining production-grade transformation layers should be second nature
- Track record of implementing testing, CI/CD, documentation standards and PR review workflows in dbt projects
- Experience building and owning streaming / real-time data pipeline infrastructure (Kafka or similar)
- 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
- Fluent with AI-assisted development workflows (Cursor, Windsurf, Claude Code) as a matter of course, it's simply how we build
- Comfortable owning an infrastructure roadmap, you can assess the current state, propose a plan and execute without being directed step-by-step
Nice to have requirements
- A data-engineering or software-engineering background with prior coding experience (Python, C++, etc.) before moving into dbt, adds rigour and variety to a team drawn mostly from the analytics / data-science side
- Semantic-layer experience (Cube, dbt Semantic Layer) and an understanding of how governed metric definitions sit on top of a transformation layer
- 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
- Full ownership of warehouse coding standards, data architecture and infrastructure
- Autonomous agents deployed and running against our data stack, a real step towards how data should operate in the new world
- Data infrastructure moved off day-to-day firefighting and onto proper engineering standards: monitored via incident.io and PagerDuty, with escalations, SLAs and documented processes in place
- Measurable improvement in the cost and efficiency of supplying data to the business, and a clear data infrastructure roadmap delivered for the following 6 months
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Interview Process
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 (i.e. your application questions).
[45mins] Interview with the Hiring Manager
You'll meet Parth, who leads our Data team. This focuses on the depth of your data-engineering and dbt experience and your ways of working. The best preparation is to know what's on your CV really well.
Take-Home Task
We'll send you a real-world task to build out. Full use of AI tooling is encouraged, it's how we work. You'll then present and defend it in the next stage.
[1hr] Assessment Review with the Data Team
A practical session where you present your take-home to a member of the data team and field questions on your architecture and decisions.
[45mins] Bar Raiser / Leadership Interview
A final conversation with a member of our engineering leadership to discuss motivations and give you the chance to ask your own questions 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
- 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
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