Analytics Engineering Manager

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

Analytics Engineering is a central team working across Zopa's products rather than being embedded within one product area. The team has grown significantly as demand for high-quality, consumable datasets has increased. It works closely with Analytics and Data Engineering and is building stronger partnerships across Product and Engineering. The team is friendly, supportive and highly autonomous, with an expectation of high-quality delivery at speed. As the team grows, this role will provide closer leadership and development while helping shape Analytics Engineering standards, tooling and ways of working as Zopa's data and AI capabilities evolve.

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A Day In The Life:

• Lead, coach and develop a high-performing Analytics Engineering team.

• Provide regular feedback, development support and effective performance management.

• Own prioritisation across incoming requests, strategic initiatives, team capacity and longer-term projects.

• Establish and deliver an Analytics Engineering roadmap aligned with Product and business priorities.

• Improve the quality, reliability and scalability of analytical data products.

• Embed strong practices across data modelling, testing, observability, documentation and CI/CD.

• Work across Product, Engineering, Analytics and business teams to ensure Analytics Engineering delivers measurable value.

• Partner with Data Engineering, Data Science and Analytics to contribute to a cohesive data platform.

• Represent Analytics Engineering in wider engineering discussions and champion better data practices.

• Drive continuous improvement, knowledge sharing and adoption of emerging technologies, including AI-assisted development.

About You:

• You have experience leading Analytics Engineering, BI Engineering or Data Engineering teams.

• You have previous individual-contributor experience in Analytics Engineering.

• You have a track record of building and developing high-performing engineering teams.

• You have strong SQL and data-modelling expertise and experience with modern transformation tools such as dbt.

• You have experience working with cloud data platforms such as Snowflake, BigQuery or Databricks.

• You have defined technical roadmaps and delivered measurable business outcomes.

• You can build trust and influence both technical and business stakeholders.

• You understand modern engineering practices including testing, observability, documentation and CI/CD.

• You have sufficient technical depth to identify systemic problems and contribute credibly to solutions alongside senior technical colleagues.

• You can provide effective leadership to a highly autonomous team without micromanaging.

Added Bonus:

• Experience building semantic or metrics layers.

• Experience applying AI to Analytics Engineering workflows or developing AI-ready data products.

• Knowledge of orchestration and ingestion tools such as Airflow, Dagster or Fivetran.

• Python experience for automation or data engineering tasks.

• Experience working in regulated financial services or another highly data-driven organisation.

• Snowflake experience, given its introduction at Zopa.

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At Zopa we value flexible ways of working.

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

Diversity Statement

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.

Our approach to AI in interviews

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.