SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Lendable is a UK unicorn fintech company (800+ employees, profitable since 2017, backed by Balderton Capital and Goldman Sachs) rebuilding consumer finance products—loans, credit cards, and car finance—with modern technology. The company is expanding into the UK and US markets.
You will join the UK Cards team as an Analytics Engineer, contributing to the analytical foundation and data infrastructure. You'll work closely with analysts, product teams, backend engineers, and business stakeholders to improve how data is structured, transformed, and consumed across the organization.
Key responsibilities:
- Build and improve data models supporting lending decisions, pricing, portfolio analysis, and investor reporting
- Champion analytics engineering standards and contribute to improving the team's data culture
- Support and collaborate with analysts at different technical levels, translating requirements into robust pipelines
- Triage and resolve issues affecting the analytics pipeline or data trust; contribute ideas to improve efficiency, reliability, and cost-effectiveness of the transformation pipeline
The role is fundamentally about building a strong analytical foundation: enabling teams to move from question to insight quickly while maintaining high standards around data quality, scalability, and maintainability.
Tech stack: SQL, Snowflake, dbt, Fivetran, and Claude.
Interview process: initial engineer call, 15-minute cognitive assessment, 30-minute hiring manager chat, 60-minute technical interview, 60-minute culture interview.
Requirements:
- Strong data modelling skills and 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 identifying areas of data workflows that could be improved and suggesting solutions
- Collaborative working style and clear communication across technical and non-technical stakeholders
- Comfort using AI tools effectively to move faster, improve quality, and strengthen analytical and engineering workflows
