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hackajob

Senior Analytics Engineer

London
Posted about 21 hours ago
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hackajob is collaborating with Lendable to connect them with exceptional professionals for this role.

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

What you'll be doing

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.

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.

Start with a chat, not a search bar

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.

P

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 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.

Responsibilities

  • 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.

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 strong analytics engineering fundamentals and the ability to apply them pragmatically in a fast-moving environment.

Essential

More specifically, we’re looking for:

  • Strong SQL skills
  • Strong experience with ELT pipelines and transformation at scale, using dbt.
  • Excellent stakeholder management skills, with a proven ability to influence and negotiate with both technical and non-technical stakeholders
  • 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.

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Desirable

  • 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.
  • Fintech or scale-up experience

Interview process

  • Initial call with an engineer
  • 15-minute cognitive assessment
  • 90-minute technical interview (Interactive architecture/design exercise)
  • 60-minute culture interview with primary stakeholders

Life at Lendable

  • 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
  • Socials & connection: opportunities and events to come together, socialise, and get to know each other beyond the office walls
  • Health coverage: support for your physical and mental wellbeing, including private health cover
  • Retirement & savings: long-term financial wellbeing through retirement savings plans
  • Employee referral programme: earn a competitive bonus when you refer successful new team members
  • 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

Please note: The availability and details of specific benefits vary by location and role. For more information, please speak to your Talent Partner.

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Skills

SQL
ELT Pipelines
Data Modelling
Stakeholder Management
Data Quality
Machine Learning
AI Tools
Cloud Data Warehouse

Location

London, England, United Kingdom

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