About the role
We're looking for a Junior Analytics Engineer to join the analytical foundation for our US Cards team, the fastest-developing area of the business. In this role, you’ll work closely with analysts, product teams, backend engineers, and business stakeholders to help improve how data is structured, transformed, and consumed across the company.
The role is fundamentally about contributing to a strong analytical foundation: helping teams move from question to insight quickly, while improving data quality, scalability, and maintainability.
You'll be supported by experienced engineers and given the space to grow — picking up new skills, deepening your SQL and dbt knowledge, and building confidence across a modern data stack.
What you'll be doing
Contributing to the data models that support credit decisions, origination, portfolio analysis, and investor reporting.
Building and improving dbt models and transformations, guided by senior engineers and in close collaboration with analysts and stakeholders.
Acting as a bridge between analysts, backend engineers, product teams, and the data platform team to help ensure data is modelled and used effectively.
Identifying opportunities to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time.
Supporting the scaling of our data infrastructure as the business grows.
Our modern data stack
You’ll work with a modern analytics stack centred around SQL, Snowflake, dbt, Fivetran and Claude.
What we're looking for
We're looking for someone with solid analytics engineering fundamentals - or the drive to develop them - and the curiosity to apply them in a fast-moving environment.
More specifically, we’re looking for:
Essential:
Solid SQL skills and a willingness to keep improving them.
Some hands-on experience with dbt or ELT pipelines.
A collaborative working style and clear communication across technical and non-technical stakeholders.
A growing understanding of data modelling and how analytical datasets should be structured for reliability and usability.
Comfort using AI tools to move faster and improve the quality of your work.
Desirable:
Experience with Snowflake or another modern cloud data warehouse.
An interest in learning from and eventually supporting analysts through shared patterns and good practices.
Fintech or scale-up experience
Interview process
Initial call
Take Home Task
Technical Interview
Culture Interview
