Zopa4mo ago

Senior Analytics Engineer

United Kingdom·London,LondonEmployee - Permanentsenior

Data ScienceData EngineeringData EngineerAnalytics EngineerDataData & AI

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Quick Summary

Key Responsibilities

This is a hands-on technical leadership role focused on raising standards, shaping technical direction, and increasing modelling maturity across domains — without formal line management responsibility.

Technical Tools

dbtsqlci-cdcode-reviewmentoring

Our Story

Hello there. We’re Zopa.

We started our journey back in 2005, building the first ever peer-to-peer lending company. Fast forward to 2020 and we launched Zopa Bank. A bank that listens to what our customers don’t like about finance and does the opposite. We’re redefining what it feels like to work in finance. Our vision for a new era of banking puts people front and centre — we’ve built a business that empowers everyone to aim high, every day, to move finance forward. Find out more about our fantastic offerings at Zopa.com!

We’re incredibly proud of our achievements and none of it would be possible without the amazing team here. It’s not just industry awards we’re winning, we’ve also been named in the top three UK’s Most Loved Workplaces.

If you embrace unconventional challenges, are unafraid to think differently and are driven to make an outsized impact, you’ll thrive here at Zopa, so join us, and make it count. Want to see us in action? Follow us on Instagram @zopalife

The Team

The Analytics Engineering team is a key part of the broader Data, Analytics & AI organisation within Technology. Our mission is to transform data into high-quality, trusted, and scalable data products that empower analytics, product, finance, risk, and commercial teams to make smarter decisions for our customers.

We work closely with Data Engineers, Analysts, Data Scientists, ML Engineers and stakeholders across the business. As Zopa continues to align Analytics Engineering to business domains, we are embedding domain ownership, ensuring our data models and semantic layers reflect real-world business boundaries and needs.

The Role

You'll own and deliver the most complex modelling challenges , help to define and evolve best practices for dbt, modelling, governance and cost optimisation. You'll act as a technical authority within analytics engineering, whilst mentoring and enabling others to raise their technical bar. You'll also shape how domain data products are designed, owned, and consumed across Zopa.

Evolve the technical direction for Analytics Engineering in partnership with the Head of AE

Develop scalable modelling patterns aligned to our data mesh and domain-based ownership approach

Raise engineering standards across testing, CI/CD, documentation, version control and code review

Lead by example in applying data governance and risk standards across all data products

Design and build robust, production-grade, cost-efficient dbt models for high-impact domains

Own modelling strategy for key business areas, ensuring consistency, maintainability, and scalability

Optimise data marts for performance, reliability and long-term growth

Break down complex domain problems into scalable data products

Evaluate trade-offs between cost, performance, maintainability and delivery speed

Take end-to-end ownership of high-impact modelling initiatives

Partner with product teams and domain owners to translate business needs into clean, well-documented data models

Ensure cross-domain consistency and reusability of shared entities

Advocate for domain ownership and clear data contracts

Simplify overly complex modelling areas and reduce unnecessary warehouse costs

Mentor Analytics Engineers and Analysts, helping them deepen their modelling and engineering expertise

Provide high-quality, empathetic and actionable code reviews

Share knowledge at scale through documentation, technical sessions or playbooks

Thrives in a developing environment where not everything is defined

Communicates clearly with engineers, product teams and business stakeholders

Can influence without authority

Has strong problem-solving and critical thinking skills

Is comfortable making decisions in ambiguity

Enjoys mentoring and raising standards

Is proactive about identifying and solving structural problems

Embraces continuous learning and technological advancement

Significant experience as a Senior or Lead Analytics Engineer

Demonstrable experience setting technical standards or shaping modelling practices

Strong expertise in SQL and building production-grade, cost-efficient dbt models

Experience working in modern cloud data environments

Strong understanding of data governance and risk considerations

Experience working cross-functionally in product-led environments

Solid understanding of CI/CD, Git-based workflows, and version-controlled deployments

#LI-JH1

We value face-to-face collaboration and a good work-life balance. This hybrid role requires you to come to our London office 2-3 days a week.

You'll also have the option of working from abroad for up to 120 days a year!* But no matter where you are, we’ll make sure you’ve got everything you need to thrive, both in your work and home life, from day one.

*Subject to having the right to work in the country of choice

Zopa is proud to offer a workplace free from discrimination. Diversity of experience, perspectives, and backgrounds leads to better products for our customers and a unique company culture for our people. We are made up of nearly 50 nationalities, have a DE&I forum made up of Zopians wanting to make a difference and we are proud of our culture where everyone can bring their full self to work. Our approach to DE&I is reflected in our hiring process so please let us know if you require any reasonable adjustments.

At Zopa, AI isn't something we're testing out — it's part of how we work every day. As a proud partner of Jobs 2030, we're committed to building AI fluency across our workforce, and we expect Zopians to use AI as part of how they do their jobs.

Because of that, we want to be transparent about how we think about AI use during our hiring process.

Behavioural and competency-based interviews: please don't use AI. These conversations are designed to understand you — your experiences, your judgment, and how you've approached real situations. An AI-generated answer can't tell us that. What it can do is get in the way of us finding out whether we're the right fit for each other.

Technical interviews: it depends on the role. Some technical stages actively welcome AI use, others don't. Your Talent Partner will let you know what's expected at each stage. Where AI is part of the assessment, we'll be interested not just in the outcome, but in how you used it – the tools you chose, your reasoning, and the decisions you made along the way.