At a Glance
- Tasks: Own and enhance NALA's data transformation layer for impactful analytics.
- Company: Join a fast-growing fintech startup revolutionising global payments.
- Benefits: Generous leave, learning budget, enhanced parental leave, and fun monthly socials.
- Other info: Dynamic team with opportunities for mentorship and career growth.
- Why this job: Make a real difference in global payments while developing your skills.
- Qualifications: 4+ years of experience with dbt, SQL, Python, and data modelling.
The predicted salary is between 36000 - 60000 £ per year.
NALA is building Payments for the Next Billion. Faster, smarter, and fairer transfers for everyone. Since 2022, we have 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; and Rafiki, our B2B payments infrastructure, 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.
Your Mission
As Senior Analytics Engineer, you will 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.
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: Full ownership of warehouse coding standards, data architecture and infrastructure. Measurable improvement in the cost and efficiency of supplying data to the business. A clear data infrastructure roadmap delivered for the following 6 months.
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 4 stages:
- [30mins] Interview with the Talent Team - We want to understand your experience and motivations.
- [1hr] Live Assessment with the Data Team - A practical exercise based on a realistic task, completed and discussed with a member of the data team.
- [1hr] Interview with the Hiring Manager - A deeper dive into your experience, technical depth and approach to the role.
- [45mins] Senior Leadership Interview - A final conversation with a member of our senior leadership team to discuss motivations and ask your own questions.
Benefits
- 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 off 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 on how you save for your future!
- 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.
Senior Analytics Engineer employer: Nala
This secondary school in Essex is an exceptional employer, offering a vibrant work culture that fosters collaboration and innovation among its staff. With a strong commitment to professional development, the school provides ample opportunities for growth and advancement, allowing educators to make a meaningful impact on students' lives while leading a dedicated Science team. The supportive environment encourages ambitious leaders to thrive and contribute to raising educational standards across all key stages.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at NALA or similar companies. A friendly chat can open doors and give you insights that might just land you an interview.
✨Tip Number 2
Prepare for your interviews by practising common questions related to analytics engineering. Think about how your experience with dbt and Snowflake can shine through. We want to see your skills in action!
✨Tip Number 3
Showcase your projects! If you've worked on any cool data transformation projects, be ready to discuss them. We love seeing how you've tackled challenges and what impact your work has had.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in being part of our mission at NALA.
We think you need these skills to ace Senior Analytics Engineer
Some tips for your application 🫡
Be Yourself:When you're filling out your application, let your personality shine through! We want to get to know the real you, so don’t be afraid to show your passion for analytics and how you can contribute to our mission.
Tailor Your Experience:Make sure to highlight your relevant experience with dbt, SQL, and data modelling. We’re looking for specific examples that demonstrate your skills and how they align with what we do at NALA.
Keep It Clear and Concise:While we love a good story, keep your application straightforward. Use clear language and avoid jargon where possible. We want to understand your journey without getting lost in the details!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and kickstart the process. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Nala
✨Know Your Data Tools Inside Out
Make sure you’re well-versed in dbt, Snowflake, and any other tools mentioned in the job description. Brush up on your SQL and Python skills, and be ready to discuss how you've used these tools in past projects. This will show that you can hit the ground running!
✨Prepare for Practical Assessments
Since there's a live assessment with the Data Team, practice real-world scenarios that might come up. Think about how you would refactor a transformation layer or optimise a data pipeline. Being able to demonstrate your thought process during this exercise is key!
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific challenges you've faced in previous roles, especially around data architecture and cost optimisation. NALA values urgency and deep thinking, so highlight how you’ve tackled complex problems and what impact your solutions had.
✨Ask Insightful Questions
During your interviews, especially with senior leadership, ask questions that show your interest in NALA's mission and future. Inquire about their data strategy or how they envision AI impacting their analytics. This not only shows your enthusiasm but also helps you gauge if the company aligns with your values.
