Analytics Engineering Manager
London, England, United Kingdom · Full Time
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- Salary
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- Openings
- 1
- Posted
- 2 days ago
- Work mode
- In office
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Job description
A Day In The Life:
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Lead, coach and develop a high-performing Analytics Engineering team.
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Provide regular feedback, development support and effective performance management.
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Own prioritisation across incoming requests, strategic initiatives, team capacity and longer-term projects.
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Establish and deliver an Analytics Engineering roadmap aligned with Product and business priorities.
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Improve the quality, reliability and scalability of analytical data products.
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Embed strong practices across data modelling, testing, observability, documentation and CI/CD.
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Work across Product, Engineering, Analytics and business teams to ensure Analytics Engineering delivers measurable value.
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Partner with Data Engineering, Data Science and Analytics to contribute to a cohesive data platform.
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Represent Analytics Engineering in wider engineering discussions and champion better data practices.
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Drive continuous improvement, knowledge sharing and adoption of emerging technologies, including AI-assisted development.
About You:
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You have experience leading Analytics Engineering, BI Engineering or Data Engineering teams.
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You have previous individual-contributor experience in Analytics Engineering.
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You have a track record of building and developing high-performing engineering teams.
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You have strong SQL and data-modelling expertise and experience with modern transformation tools such as dbt.
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You have experience working with cloud data platforms such as Snowflake, BigQuery or Databricks.
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You have defined technical roadmaps and delivered measurable business outcomes.
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You can build trust and influence both technical and business stakeholders.
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You understand modern engineering practices including testing, observability, documentation and CI/CD.
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You have sufficient technical depth to identify systemic problems and contribute credibly to solutions alongside senior technical colleagues.
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You can provide effective leadership to a highly autonomous team without micromanaging.
Added Bonus:
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Experience building semantic or metrics layers.
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Experience applying AI to Analytics Engineering workflows or developing AI-ready data products.
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Knowledge of orchestration and ingestion tools such as Airflow, Dagster or Fivetran.
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Python experience for automation or data engineering tasks.
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Experience working in regulated financial services or another highly data-driven organisation.
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Snowflake experience, given its introduction at Zopa.
Industry
Financial Services