Location
London
Hours
Full Time
Salary
Competitive, commensurate with experience
About the Role
NALA is building Payments for the Next Billion — faster, smarter, and fairer transfers for everyone. Since 2022, we have grown 120x, expanded our team from 9 to 150+, raised over $50M from top-tier investors, and were named to the Forbes Fintech 50 in 2025 and 2026. Our core products include NALA, a consumer app enabling cheaper, faster, and more reliable cross-border payments from the UK, US, and EU to Africa and Asia, and Rafiki, our B2B payments infrastructure powering global giants like MoneyGram and Western Union. Our team includes alumni from Wise, Stripe, Monzo, Revolut, and CashApp, who operate with urgency, deep thought, and a customer-first mindset.
As Senior Analytics Engineer, you will own key domains in our data transformation layer using dbt and Snowflake, ensuring models are dependable, well tested, documented, and semantically rich enough for both humans and machines to trust without interpretation. You will collaborate closely with our existing Analytics Engineer to set modelling standards and drive excellence across the team. This role requires experience solving real business problems with dbt and a strong understanding of dimensional modelling in the modern data warehouse, especially as AI increasingly queries data autonomously.
Your responsibilities include end-to-end ownership of modelling and transformation, setting and defending modelling methodology, making models agent-ready, managing streaming and near-real-time pipelines, enforcing coding and testing standards, optimising warehouse performance and cost, deploying autonomous agents, and mentoring analysts to raise engineering standards across the team.
This role offers the opportunity to shape the future of global payments data infrastructure and work at the forefront of AI-driven analytics.
Interview Process
Applications are submitted through our ATS Workable with no cover letter required. The interview process includes:
- 30-minute interview with the Talent Team to discuss experience and motivations
- 45-minute interview with the Hiring Manager focusing on dbt usage, modelling philosophy, and AI integration
- Take-Home Task scoped for 2-3 hours with a week to complete, full AI tooling use encouraged
- 1-hour technical deep dive presenting your take-home task to the data team
- 45-minute final conversation with a leadership team member covering track record, ambition, and questions about the business
Benefits
Private Medical Insurance through a leading UK provider
27 Days Off plus UK Bank Holidays
Birthday Leave: a bonus day off to celebrate your special day
Enhanced Parental Leave: 16 weeks full pay for primary caregiver, 4 weeks full pay for secondary caregiver (after 6-month probation)
Enhanced Pension: salary sacrifice pension scheme via Penfold
Life Insurance: 3x salary cover
Global Workspace: access to WeWork locations worldwide
Learning Budget: $1000 annually for learning and development
Sarabi: themed snacks and Friday lunch to build team relationships
Monthly Socials: regular social events
Free Coffee: barista-style coffee available onsite
Experience
- Minimum three years of production dbt experience with demonstrated judgement in modelling methodology
- Proven track record of solving business problems using dbt, including ownership of modelling work that influenced decisions
- Strong understanding of dimensional modelling concepts such as grain, facts, dimensions, fan-out, and double counting
- Experience building and owning streaming or near-real-time pipeline infrastructure (Kafka or similar)
- Proficient in SQL, Python, and data warehouse fundamentals with experience in Snowflake or Databricks, including query performance tuning and cost optimisation
- Established engineering standards in dbt including testing, CI/CD, documentation, and PR review workflows
- Experience deploying and maintaining autonomous agents and AI-native tooling (e.g., Cursor, Windsurf, Claude Code)
- Ability to own a roadmap: assess current state, propose plans, scope, and execute independently
About You
- You have a considered and practical view on what makes a model trustworthy for autonomous querying by agents
- You experiment with AI and data tools beyond your day job to understand their differences and capabilities
- You are proactive, self-directed, and comfortable mentoring others to raise team standards
- You thrive in a fast-growing startup environment and are motivated by impact and ownership
Qualifications
- While no formal qualifications are strictly required, a background in data engineering or software engineering is a strong advantage
- Experience with semantic layers (Cube, Snowflake Semantic Views, dbt Semantic Layer) and governed metric definitions is desirable
- Familiarity with modern BI and notebook platforms such as Hex
- Experience in fintech, payments, or regulated environments where data accuracy is critical
- Knowledge of experimentation frameworks and product analytics is a plus