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Lendable

Analytics Engineer

London
Posted 24 days ago
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About Lendable

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:

  • One of the UK’s newest unicorns with a team of just over 700 people
  • Among the fastest-growing tech companies in the UK
  • Profitable since 2017
  • Backed by top investors including Balderton Capital and Goldman Sachs
  • Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot)

So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance. We get money into our customers’ hands in minutes instead of days. 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.

Join us if you want to:

  • Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1
  • Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
  • Build the best technology in-house, using new data sources, machine learning, and AI to make machines do the heavy lifting

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.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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It searches the market for you

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Strong

Only hits

No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.

You'll contribute to the modelling layer, help improve how the business works with data, and support your team in keeping our warehouse a reliable, strategic asset for the business.

Responsibilities

Core Duties

  • 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

Technical Work

  • Work with a modern data stack centred around:
    • SQL
    • Snowflake
    • dbt
    • Fivetran
    • Claude

Requirements

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.

Key Skills and Experiences

  • 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

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Interview Process

  • Initial call with an engineer
  • 15-minute Cognitive Assessment
  • Onsite or video interview lasting 90 minutes, comprising:
    • Introduction of the team and kind of work you could be doing daily
    • Interactive architecture/design exercise
    • Questions you may have about the company and role
    • 60-minute chat with the primary stakeholders of the role
    • Cultural/behavioural questions
    • Product mindset and the ability to collaborate and communicate

Life at Lendable

  • Employee benefits and opportunities:
    • Join a winning team — scale up one of the world’s most successful fintech companies
    • Flexible working: 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
    • Socials & connection: opportunities and events to socialise and get to know each other beyond the office walls

Wellbeing and Financial Support

  • Health coverage: support for your physical and mental wellbeing through private health cover
  • Retirement & savings: financial wellbeing through retirement savings plans
  • Employee referral programme: earn a competitive bonus when you refer successful new team members

Office and Commuting

  • Office meals & snacks: enjoy a fully stocked kitchen plus complimentary lunches prepared by in-house chefs on in-office days at select locations
  • Sustainable commuting: cycle-to-work and electric vehicle salary sacrifice schemes available in select locations

Note: The availability and details of specific benefits vary by location and role. Please speak to your Talent Partner for more details.

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Skills

Data Modelling
ELT Pipelines
dbt
Snowflake
SQL
Cloud Data Warehousing
Stakeholder Communication
AI Tool Integration

Location

London, England, United Kingdom

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