At a Glance
- Tasks: Own and improve data models for lending decisions and support analysts in a fast-paced environment.
- Company: Join Lendable, a rapidly growing fintech company revolutionising credit and savings.
- Benefits: Flexible working, competitive salary, and opportunities for professional growth.
- Other info: Be part of a winning team with regular social events and a collaborative culture.
- Why this job: Make a real impact in the fintech space while working with cutting-edge technology.
- Qualifications: Strong SQL skills and experience with ELT pipelines using dbt.
The predicted salary is between 60000 - 80000 £ per year.
Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start. Among the fastest-growing tech companies in the UK, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards, and car finance. We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.
We're looking for a Senior Analytics Engineer to own the analytical foundation for our US Loans team, the fastest-growing area of the business. In this role, 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 operate with a high degree of ownership, helping shape the modelling layer, improving how analysts work with data, and ensuring our warehouse remains a strategic asset for the business.
- Owning and improving the data models that support lending decisions, pricing, portfolio analysis, and investor reporting.
- Driving the development of our dbt models and transformation layer, working with analysts and stakeholders to improve the speed and quality of insight generation.
- Helping define good modelling patterns, architecture, and implementation standards across the analytics engineering layer.
- Supporting and mentoring analysts at different technical levels, helping them build stronger engineering habits and become more effective with data.
- Acting as a bridge between analysts, backend engineers, product teams, and the data platform team to make sure data is generated, modelled, and used effectively.
- Identifying opportunities to improve the efficiency, reliability, and cost-effectiveness of our transformation pipeline over time.
- Scaling our data infrastructure to proactively support the requirements of a rapidly growing business.
You’ll work with a modern analytics stack centred around SQL, Snowflake, dbt, Fivetran, and Claude. We’re looking for someone with strong analytics engineering fundamentals and the ability to apply them pragmatically in a fast-moving environment.
- Strong SQL skills.
- Strong experience with ELT pipelines and transformation at scale, using dbt.
- Strong data modelling skills and a good understanding of how analytical datasets should be structured for reliability and usability.
- Good judgement in balancing longer-term platform improvements with day-to-day business needs.
- The ability to spot inefficiencies in existing data workflows and improve them independently.
- Comfort using AI tools effectively to move faster, improve quality, and strengthen day-to-day analytical and engineering workflows.
- Experience with Snowflake or another modern cloud data warehouse.
- An interest in helping analysts raise their technical bar through support, mentoring, and better shared patterns.
Winning team: the opportunity to scale up one of the world’s most successful fintech companies.
Flexible working: flexible approach tailored to each role. Hybrid roles require three days in-office weekly; fully remote roles include regular opportunities for in-person connection through socials and off-sites.
