Senior Analytics Engineer

About Zego

At Zego, we understand that traditional motor insurance holds good drivers back. It's too complicated, too expensive, and it doesn't reflect how well you actually drive. Since 2016, we have been on a mission to change that by offering the lowest priced insurance for good drivers.

From van drivers and gig workers to everyday car drivers, our customers are the driving force behind everything we do. We've sold tens of millions of policies and raised over $200 million in funding. And we're only just getting started.

Overview of Team

The Data team at Zego partners with the business to maximise the value of data. Through collaborative engineering and analytics, we ensure our teams are equipped with relevant and reliable insights to drive fast, confident and high-impact decisions.

We're a small, dynamic team responsible for all aspects of data — availability, quality, integration, problem formulation, success metrics, reporting, and analytics. We take an AI-First approach: AI is now central to how we work, and we focus less on hand-writing code and more on framing problems, specifying intent, and owning the trust infrastructure (testing, contracts, observability) that the business relies on.

About the Role

We're looking for a Senior Analytics Engineer to help lead how we build. The craft is changing fast: AI tools now handle a lot of the heavy lifting of writing SQL and boilerplate, which means the job is increasingly less about typing code and more about understanding the business problem deeply, specifying what "good" looks like, and taking ownership of whether what we ship is correct and trustworthy.

You'll own the design of the data models the whole business depends on, set the standards and patterns the rest of the team builds to, and help shape how we put AI to work — not just using AI-assisted workflows, but improving the tooling and practices around them. You'll be a technical anchor for the team: raising the bar through review, mentoring, and pairing, and levelling up the engineers around you.

You'll bring deep technical expertise, a natural curiosity for data, and a strong instinct to question whether a number is actually right — not just whether a query ran. You're as comfortable making architectural decisions and driving them to a conclusion as you are engaging stakeholders to understand what they really need.

What You Will Be Doing

Data Modelling & Engineering (AI-assisted)

Partner with stakeholders to understand business needs and translate ambiguous problems into clear, well-scoped requirements

Own the design of robust, scalable data models and exposures in dbt, Snowflake, and Looker — and set the patterns and conventions the wider team reuses

Make and document the architectural decisions and trade-offs that shape our data platform

Use AI-assisted development to move faster — spending less time hand-writing boilerplate and more time specifying intent, reviewing, and refining — and help define how the team gets the best out of these tools

Read, debug, and critically review SQL (including AI-generated code), so we ship models we can trust

Wrangle and integrate data from multiple third-party sources (e.g. Amplitude, Segment, Google Ads)

Data Quality, Trust & Operations

Define and champion the "trust infrastructure" — the testing, data-contract, and observability standards the team works to — that lets the business depend on our models

Own quality, reliability, and stability across data models and pipelines

Create secure, efficient data shares for external partners (e.g. S3, SFTP, Snowflake Data Sharing)

Drive the continuous improvement of our data platform, tooling, and ways of working — including how we use AI

Stakeholder Enablement & Technical Leadership

Act as a technical anchor for the team — raising the bar through code review, mentoring, and pairing

Support business users across the company to promote a culture of data-driven decision-making

Translate complex business question