{"id":99801,"url":"https://alion.io/job/zopa-analytics-engineering-manager","title":"Analytics Engineering Manager","company":{"id":6054,"name":"Zopa","domain":"zopa.com","url":"https://alion.io/company/zopa","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":88,"open_postings":16,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":88000,"max_usd":191000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Fivetran","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-08-13T12:29:48Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-16T15:13:25Z","board_verified":true,"closed_at":null,"days_open":64,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":63},"description":"The Team\nAnalytics 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.\nA Day In The Life:\nLead, coach and develop a high-performing Analytics Engineering team.\n\nProvide regular feedback, development support and effective performance management.\n\nOwn prioritisation across incoming requests, strategic initiatives, team capacity and longer-term projects.\n\nEstablish and deliver an Analytics Engineering roadmap aligned with Product and business priorities.\n\nImprove the quality, reliability and scalability of analytical data products.\n\nEmbed strong practices across data modelling, testing, observability, documentation and CI/CD.\n\nWork across Product, Engineering, Analytics and business teams to ensure Analytics Engineering delivers measurable value.\n\nPartner with Data Engineering, Data Science and Analytics to contribute to a cohesive data platform.\n\nRepresent Analytics Engineering in wider engineering discussions and champion better data practices.\n\nDrive continuous improvement, knowledge sharing and adoption of emerging technologies, including AI-assisted development.\n\nAbout You:\nYou have experience leading Analytics Engineering, BI Engineering or Data Engineering teams.\n\nYou have previous individual-contributor experience in Analytics Engineering.\n\nYou have a track record of building and developing high-performing engineering teams.\n\nYou have strong SQL and data-modelling expertise and experience with modern transformation tools such as dbt.\n\nYou have experience working with cloud data platforms such as Snowflake, BigQuery or Databricks.\n\nYou have defined technical roadmaps and delivered measurable business outcomes.\n\nYou can build trust and influence both technical and business stakeholders.\n\nYou understand modern engineering practices including testing, observability, documentation and CI/CD.\n\nYou have sufficient technical depth to identify systemic problems and contribute credibly to solutions alongside senior technical colleagues.\n\nYou can provide effective leadership to a highly autonomous team without micromanaging.\n\nAdded Bonus:\nExperience building semantic or metrics layers.\n\nExperience applying AI to Analytics Engineering workflows or developing AI-ready data products.\n\nKnowledge of orchestration and ingestion tools such as Airflow, Dagster or Fivetran.\n\nPython experience for automation or data engineering tasks.\n\nExperience working in regulated financial services or another highly data-driven organisation.\n\nSnowflake experience, given its introduction at 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