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Zopa is a digital bank founded in 2005 as the first peer-to-peer lending platform, now operating as Zopa Bank with a mission to redefine modern banking by putting customers first. The company has been recognized as one of the UK's Most Loved Workplaces and operates with nearly 50 nationalities on staff.
The Analytics Engineering team is a central, cross-product function that has grown significantly as demand for high-quality datasets has increased. The team works closely with Analytics, Data Engineering, Product, and Engineering teams to build and maintain analytical data products.
In this Manager role, you will lead and develop a high-performing Analytics Engineering team, providing coaching, feedback, and performance management. You'll own prioritization across incoming requests, strategic initiatives, and longer-term projects while establishing an Analytics Engineering roadmap aligned with Product and business priorities. Key responsibilities include improving the quality, reliability, and scalability of analytical data products; embedding strong practices around data modeling, testing, observability, documentation, and CI/CD; and working cross-functionally to ensure Analytics Engineering delivers measurable value.
You'll represent Analytics Engineering in wider engineering discussions, champion better data practices, and drive continuous improvement and knowledge sharing. The role involves partnering with Data Engineering, Data Science, and Analytics teams to contribute to a cohesive data platform, with particular focus on emerging technologies including AI-assisted development.
Required experience includes leading Analytics Engineering, BI Engineering, or Data Engineering teams, with strong individual-contributor background in Analytics Engineering. You should have a track record of building high-performing teams, deep SQL and data-modeling expertise, and experience with modern transformation tools like dbt. Cloud data platform experience (Snowflake, BigQuery, or Databricks) is essential, along with the ability to define technical roadmaps and deliver measurable business outcomes. You must build trust and influence both technical and business stakeholders, understand modern engineering practices, and provide effective leadership to autonomous teams.
Bonus qualifications include experience with semantic or metrics layers, AI applications in Analytics Engineering workflows, orchestration tools (Airflow, Dagster, Fivetran), Python automation, regulated financial services background, and Snowflake expertise.
The role is hybrid, requiring 2-3 days per week in the London office with flexibility to work abroad up to 120 days annually.
