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Analytics EngineerZego · London
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Zego

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Analytics Engineer

🗓️ Posted 2026-09-16 London Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 4,583 GBP–6,667 GBP/mo

About the Company

Zego is on a mission to 'do the good thing, not the insurance thing,' building real-time, AI-driven infrastructure that powers innovative insurance. The company describes itself as leading the AI evolution in insurance, with AI as its operating reality rather than a roadmap item. It backs high-performance builders who want ownership, early responsibility, and the chance to see their ideas reach millions of drivers, and avoids bureaucracy. Zego's hybrid working model balances face time and focus, with offices in central London or Halifax, quarterly team gatherings, and an annual all-company gathering.

Responsibilities

  • ● Translate business needs into clear, well scoped requirements, so every model you build answers a question someone is actually asking.
  • ● Build and maintain robust data models and exposures in dbt, Snowflake and Looker, using AI-assisted development to spend less time on boilerplate and more on specifying intent, reviewing and refining.
  • ● Review and debug SQL critically, including AI-generated code, so we ship only models we can trust and document the decisions and trade-offs behind them.
  • ● Extend the trust infrastructure of tests, data contracts and observability that keeps our models and pipelines reliable and stable.
  • ● Integrate data from third-party sources such as Amplitude, Segment and Google Ads and create secure, efficient data shares for external partners.
  • ● Enable self-service in our BI tools, so business users answer their own questions instead of queuing for the data team.
  • ● Improve our data platform, tooling and ways of working, including how the team puts AI to work.

Requirements

  • ● Analytics engineering experience: around 1 to 3 years as an Analytics Engineer or in a similar role.
  • ● Solid SQL: comfortable reading, writing, debugging and critically reviewing queries, including AI-generated ones.
  • ● Modern data platforms: familiarity with tools such as Snowflake, dbt, Looker, AWS or GCP, version control with Git and ideally some Python for data or analytics engineering tasks.
  • ● A meticulous eye for detail: strong problem-solving instincts and a healthy habit of asking whether a number is actually right, not just whether the query ran.
  • ● Commercial curiosity: an interest in the why behind the numbers and the ability to connect data work to business outcomes.
  • ● Work AI-first: use AI daily, arrive curious, experiment fast, and take ownership of getting good quickly; people who wait to be trained will find this uncomfortable.

Benefits

  • ● Market-competitive salary, benchmarked against your function and reviewed every year.
  • ● Annual performance bonus, linked to company performance.