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. 

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.