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 (700+ 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 operates in the UK and US, targeting trillions in financial products currently held by legacy banking systems.
You will join the UK Cards team as an Analytics Engineer, contributing to the analytical foundation that supports lending decisions, pricing, portfolio analysis, and investor reporting. Working closely with analysts, product teams, backend engineers, and business stakeholders, you'll focus on building a strong data foundation that enables teams to move from question to insight quickly while maintaining high standards around data quality, scalability, and maintainability.
Key responsibilities include:
- Building and improving data models that support lending decisions, pricing, portfolio analysis, and investor reporting
- Championing standards and contributing to the improvement of analytics engineering culture
- Supporting and collaborating with analysts at different technical levels, translating requirements into robust pipelines
- Triaging and resolving issues affecting the analytics pipeline or downstream dataset trust
- Contributing ideas to improve efficiency, reliability, and cost-effectiveness of the transformation pipeline
The role uses a modern data stack centered on SQL, Snowflake, dbt, Fivetran, and Claude. You'll work in a hybrid model (three days in-office weekly at London location) within small teams of exceptional, resourceful people.
REQUIREMENTS:
- Strong data modelling skills with 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 with 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
