About the role
We're looking for an analytics engineer to contribute to the analytical foundation of the UK Motor team, a rapidly-growing area of the business.
You’ll work closely with analysts, product teams, backend engineers, and business stakeholders to improve how data is structured, transformed, and consumed across the company.
The role is fundamentally about building a strong analytical foundation: making it easier for teams to move from question to insight quickly, while maintaining high standards around data quality, scalability, and maintainability.
You'll contribute to the modelling layer, help improve how the business work with data, and support the team in keeping our warehouse a reliable, strategic asset for the business.
What you'll be doing
Building and improving the data models that support lending decisions, pricing, portfolio analysis, and investor reporting.
Championing standards and contributing to the improvement of our analytics engineering culture.
Supporting and collaborating with analysts at different technical levels,helping translate requirements into robust pipelines
Helping triage and resolve issues that affect the analytics pipeline or reduce trust in downstream datasets, and contributing ideas to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time.
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 and the ability to apply them pragmatically in a fast-moving environment and explain tradeoffs to stakeholders with varying technical depth.
More specifically, we’re looking for:
Strong data modelling skills and a good understanding of how analytical datasets should be structured for reliability and usability.
Strong experience with ELT pipelines and transformation at scale, ideally using dbt.
Experience with Snowflake or another modern cloud data warehouse.
Proactiveness in raising areas of data workflows that could be improved and suggesting solutions.
A collaborative working style and clear communication across technical and non-technical stakeholders.
Comfort using AI tools effectively to move faster, improve quality, and strengthen day-to-day analytical and engineering workflows
Interview process
Initial call with an engineer
15 minute Cognitive Assessment
Onsite or Video Interview lasting 90 minutes, comprising of:
Introduction of the team and kind of work you could be doing daily
Interactive architecture/design exercise
Questions you may have about the company, role, etc.
A 60 minute chat with this role's primary stakeholders
Cultural/behavioural questions
Product mindset and ability to collaborate and communicate
