Senior Analytics Engineer

Nala Inc; Greater London; Hybrid; GBP 80,000 - 110,000; Full time

Benefits offered by this job

Private Medical Insurance

27 Days Off Plus UK Bank Holidays

Birthday Leave

Enhanced Parental Leave

Enhanced Pension

Life Insurance

Global Workspace

Learning Budget

Sarabi snacks and Friday lunch

Monthly Socials

Free Coffee

Job summary

NALA is seeking a Senior Analytics Engineer to own our data domains in dbt and Snowflake, driving trustworthy, well-modelled data for decision making. You will shape dimensional patterns, ensure real-time and batch pipelines are robust, and set engineering standards across the data stack.

You will mentor analysts and collaborate with the team to advance AI-enabled analytics while owning a roadmap and delivering measurable improvements in data reliability and cost efficiency.

Qualifications

• Three or more years of production dbt experience and strong business impact.
• Clear stance on modelling methodology and dimensional patterns.
• Practical view of warehouse design for AI-driven querying.
• Hands-on use of Cursor, Windsurf, or Claude Code is highly valued.
• Experience building streaming pipelines (Kafka or similar) and batch processing.
• Strong SQL and Python fundamentals with Snowflake/Databricks exposure.
• Engineered standards in dbt: testing, CI/CD, documentation and PR review workflows.
• You can own a roadmap and deliver with initiative.

Responsibilities

• Own the modelling and transformation of data domains end to end in dbt and Snowflake.
• Define and defend modelling methodology with the team and set standards.
• Make models agent-ready: well described, consistently named and query-safe.
• Own streaming and near-real-time pipelines alongside batch transformation.
• Establish coding and testing standards, documented CI-enforced defaults.
• Optimise warehouse performance and costs through better queries and materialisation.
• Deploy autonomous agents against the data stack and monitor reliability.
• Mentor analysts on analytics-engineering practice to raise overall standards.

Skills

dbt

SQL

Python

Data modelling

Streaming data

Tools

Snowflake

Kafka

Databricks

dbt

Job description

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 rebuilding NALA's data foundation for a world where the thing asking the question might not be a person.

Analysts, operators and executives already depend on our warehouse. Increasingly, so do agents. And an agent cannot ask a colleague what a column means, cannot tell that a join has silently doubled a number, and cannot sense that a metric is defined two different ways in two different places. Data that is merely good enough for a careful human is not good enough for that. The gap between the two is where this role lives.

As Senior Analytics Engineer, you will own a set of domains in our transformation layer (dbt and Snowflake) and make them genuinely dependable, well modelled, tested, documented and semantically rich enough that both a person and a machine can get a trustworthy answer without a human in the middle to interpret. You will work alongside our existing Analytics Engineer, and between you set the modelling standard the rest of the team builds against.

This is a role for someone who has used dbt to solve real business problems, not just to build what a ticket specified, and who has a considered view on what dimensional modelling and the modern warehouse actually demand now that AI is doing the querying.

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

• You solve business problems with dbt. You can walk us through modelling work you owned that changed a decision — what the business was getting wrong, how you structured the answer, and what you deliberately chose not to build. Three or more years of production dbt is the floor; what we are actually assessing is judgement.
• You have a real position on modelling methodology. Grain, facts and dimensions, fan‑out and double counting are working tools rather than vocabulary. You can argue dimensional modelling against one big table in both directions and say where each belongs.
• You understand what the warehouse has to become in the AI age. You have a practical view of what makes a model trustworthy for an agent to query, and you have thought about it because you have tried it, not because you read about it.
• AI‑native, beyond your editor. You use Cursor, Windsurf or Claude Code daily and you have built, deployed and maintained something agentic that runs without you watching it. You experiment outside work with tools your employer doesn't use, because you want to know how they differ. This is the requirement we weight most heavily.
• Streaming ownership. You have built and owned streaming or near‑real‑time pipeline infrastructure (Kafka or similar), and can talk about what broke as readily as what worked.
• Strong SQL, Python and warehouse fundamentals, with Snowflake or Databricks experience including query performance tuning and cost optimisation.
• Track record of engineering standards in dbt — testing, CI/CD, documentation and PR review workflows that outlasted you.
• Comfortable owning a roadmap. You can assess the current state, propose a plan and execute without being directed step by step, and you scope before you dive in.

Nice to have requirements

• A data‑engineering or software‑engineering background before moving into dbt — it adds rigour and variety to a team drawn mostly from the analytics side
• Semantic‑layer experience (Cube, Snowflake Semantic Views, dbt Semantic Layer) and an understanding of how governed metric definitions sit on a transformation layer
• Experience with Hex or a similar modern BI and notebook platform
• Fintech, payments or another regulated environment where data accuracy carries real consequences
• Familiarity with experimentation frameworks and product analytics

Success in the role looks like

3‑Month Metrics

• You own your domains outright and know where the bodies are buried in them — the models people don't trust, the numbers that disagree, the queries quietly costing us money
• You have shipped modelling work that answered a real business question, and the people who asked it are using it
• Your models are documented and tested to the standard we want everywhere, and you are helping set that standard rather than following it

6‑Month Metrics

• Your domains are dependable enough that analysts and agents both answer questions from them without coming to you first, and you can show that with usage rather than assertion
• You have deployed autonomous agents against our data stack that do real recurring work, with measurable before and after
• Your streaming and batch pipelines are monitored and on proper engineering standards, with escalation paths and SLAs rather than firefighting
• You have delivered measurable cost or efficiency improvement in how your domains serve the business, and a clear view of what should happen next

Interview Process

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 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.