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
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
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
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
What we're looking for
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 technical implementation
Comfortable giving and receiving code review
Desirable - but not required
Snowflake experience
Semantic layer experience (e.g. Omni, Cube, dbt Semantic Layer)
BI tools (e.g. Omni, Tableau, Metabase)
Exposure to AI agent tooling / MCP
CI/CD for data workflows
Python/Airflow familiarity
Terraform / IaC
Benefits
Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year
Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support
Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month
Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year
Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that’s always stocked!
Our Commitment to Diversity, Equity and Inclusion
We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here.
Our Commitment to Safeguarding
Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS).
For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children’s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings.
Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.