Analytics Engineer

Skills this role screens for

About the role

Multiverse is the upskilling platform for AI and Tech adoption.

We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce.

Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.

In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe’s first EdTech double unicorn.

But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output.

Join Multiverse and power our mission to equip the workforce to win in the AI era.

What we need

We're looking for an Analytics Engineer to help build and maintain the data models that power analytics and data science across the business. You'll develop robust, scalable dbt pipelines and help evolve our data platform — ensuring data is accessible, trusted, and well-structured.

Our core platform (Snowflake, dbt, Airflow) is established and isn't changing. What is changing is the layer on top: we're rethinking our semantic and BI layer for AI/MCP-driven self-service, so analysts, stakeholders, and AI agents can query trusted metrics directly. You'll help design the models and metric definitions that make that possible.

This is also a role built around AI-assisted development. We expect you to use AI tools (e.g. Claude Code, Cursor, Copilot) as a normal part of writing dbt models, tests, and docs — while still understanding what's happening underneath, so you can catch when the tooling gets it wrong and work effectively without it.

You'll report to the Director of Data Engineering within the Data & Insight team. We're looking for someone detail-oriented, pragmatic, and hands-on — who takes ownership, moves quickly without cutting corners, and is responsive to user needs.

What you'll work on

Also

Data Modelling & Transformation

• Build and maintain dbt models, using AI coding assistants to accelerate development while retaining full understanding of the resulting logic
• Translate business requirements into scalable data models
• Design warehouse schemas using dimensional modelling (facts, dimensions, SCDs, etc.)
• Participate in design and code reviews — including reviewing AI-generated code with the same rigour as hand-written code
• Define and expose models and metrics through our semantic layer, with an eye to how AI agents will consume them via MCP

Also

Testing, Documentation, and CI/CD

• Implement dbt tests for data quality and accuracy
• Document models and metric definitions clearly, for both human and AI consumption
• Use GitHub and CI/CD pipelines, incorporating AI-assisted workflows where they add value

Also

Performance & Architecture

• Optimise dbt models and SQL queries for performance and maintainability
• Work with Snowflake on top of a data lake architecture
• Contribute to evolving our semantic/BI layer toward AI/MCP-driven self-service

Who they want

What we're looking for

Who they want

Required Skills & Experience

• Strong experience building and optimising complex SQL (joins, window functions, optimisation)
• Strong grasp of data modelling and warehouse design (Kimball-style)
• Production dbt experience, including testing and documentation
• Hands-on experience using AI coding tools (e.g. Copilot, Cursor, Claude Code) as a real part of your workflow, not occasional use
• Strong-enough fundamentals to work confidently without AI assistance and to critically assess AI-generated output
• Familiarity with version control (GitHub)
• Able to independently translate business logic into technica