Who we are:
We're one of Europe's fastest-growing fintech companies – on a mission to create the world's most empowering way to pay.
We launched the product in 2020 and achieved double unicorn status, valued at $2 billion, and since then have taken on more than 6 million customers.
Our mission is to become the best way to pay for anything, anywhere and say goodbye to credit costs for everyone. This is huge. Want to join us?
About the role.
We are looking for a Senior Analytics Engineer: Finance, to join our Finance Data team. This is a technically deep role that sits at the boundary of data engineering and finance analytics -ideal for someone who is as comfortable building robust data models and pipelines, understands financial logic and is happy to work with finance stakeholders.
You’ll bring advanced SQL skills, strong data modelling instincts, and the ability to build scalable, well-structured data infrastructure that finance teams can rely on. Finance domain knowledge is important, but your primary strength is data - you think in schemas, pipelines, and systems as much as in metrics and reports.
If you thrive on solving complex data problems, care deeply about data quality and structure, and want to work in a fast-paced team where your technical contributions directly shape how the business understands its finances - we’d love to hear from you.
Day to day responsibilities.
Data modelling & pipeline ownership: Design, build, and maintain robust data models and transformation pipelines in dbt or equivalent tooling, ensuring finance data is structured, reliable, and scalable across the warehouse.
Finance analytics: Translate complex financial logic into clean, well-documented SQL and data models that finance teams can rely on for reporting and decision-making.
Data quality & controls: Implement and maintain data quality checks, reconciliation logic, and monitoring across critical finance data pipelines, proactively identifying and resolving issues before they reach downstream consumers.
Metric governance: Define and govern core finance KPIs and metrics, ensuring a single source of truth across the business and clear documentation of definitions, logic, and ownership.
Cross-functional collaboration: Work closely with Finance, Analytics Engineering, and Product teams to ensure finance data requirements are embedded in data infrastructure from the start, and that outputs are fit for purpose across all downstream uses.
Automation & process improvement: Identify and automate manual finance data processes, improving reliability and scalability while reducing operational burden on the team.
What we're looking for...
You’re SQL-native. Complex queries, window functions, performance tuning - this is where you feel at home.
You care about correctness. Finance data has to be right - you build with that standard in mind from the start.
You’re curious about the business. You want to understand what the numbers mean, not just how to query them - and you’re comfortable picking up finance context as you go.
You take ownership. You don’t wait to be asked - you spot a problem in a pipeline and you fix it.
You communicate clearly. You can explain a complex query to a finance stakeholder and a financial concept to a data engineer.
SQL expertise: Advanced SQL is non-negotiable - you write complex queries fluently and understand performance, indexing, and warehouse-specific behaviour (e.g. BigQuery, Snowflake, or Redshift).
Data modelling: Strong grasp of data modelling concepts and hands-on experience building models in dbt or a comparable transformation framework.
Data quality mindset: Experience building and maintaining data quality checks, reconciliation logic, and monitoring in a production environment.
Experience: 3–5+ years in a data analyst, analytics engineer, or finance data role, ideally within financial services or fintech.
Stakeholder management: Comfortable working directly with finance stakeholders, translating business requirements into technical specifications and communicating data concepts clearly to non-technical audiences.
Bonus skills.
Understanding of financial reporting concepts sufficient to implement finance logic accurately and challenge requirements constructively.
Database and data modelling experience -dimensional modelling, dbt, or a comparable transformation framework -is a strong advantage and an area we’d invest in developing.
Experience with data orchestration tools such as Airflow or equivalent.
Python or another scripting language for data manipulation and automation.
Background in consumer lending, payments, or embedded finance products.
Experience working in an Analytics Engineering or hybrid analyst/engineer capacity.
Benefits.
Compensation & Savings:
Health & Wellbeing:
Private Medical Insurance including;
GP consultations (video, telephone or face-to-face).
Prescribed medication.
In-patient, day-patient and out-patient care.
Mental health support.
Optical, dental & audiological cover.
Physiotherapy.
Advanced cancer cover.
Menopause support.
Employee Assistance Programme including:
Unlimited mental health sessions.
24/7 remote GP & physiotherapy.
24/7 helpline for emotional & practical support.
Savings & discounts on everyday shopping.
1:1 personalised well-being consultations.
Gym membership discounts.
Family Friendly Policies:
Learning & Development:
Professional Qualifications.
Professional Memberships.
Learning Suite for e-courses.
Internal Training Programmes.
FCA & Regulatory training.
Workplace Perks:
