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Multiverse is Europe's first EdTech double unicorn, a $2.1bn-valued upskilling platform for AI and tech adoption. The company has partnered with 1,500+ companies and serves learners across critical AI, data, and tech skills, generating $2bn+ ROI for employers.
Role Overview
You'll join the Data & Insight team as an Analytics Engineer, reporting to the Director of Data Engineering. Your core mission is to build and maintain data models that power analytics and data science across the business. You'll develop robust, scalable dbt pipelines and help evolve the data platform to ensure data is accessible, trusted, and well-structured.
The company's core platform (Snowflake, dbt, Airflow) is stable. What's evolving is the semantic and BI layer—rethinking it 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 this possible.
This role is built around AI-assisted development. You'll use AI tools (Claude Code, Cursor, Copilot) as a normal part of writing dbt models, tests, and docs, while maintaining deep understanding to catch when tooling gets it wrong and work effectively without it.
Key Responsibilities
- Build and maintain dbt models, leveraging AI coding assistants while retaining full understanding of 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 rigorous review of AI-generated code
- Define and expose models and metrics through the semantic layer, considering how AI agents will consume them via MCP
- 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 valuable
- Optimize dbt models and SQL queries for performance and maintainability
- Work with Snowflake on top of a data lake architecture
- Contribute to evolving the semantic/BI layer toward AI/MCP-driven self-service
You'll be detail-oriented, pragmatic, and hands-on—taking ownership, moving quickly without cutting corners, and remaining responsive to user needs.
Workplace & Benefits
Hybrid role based in London with three days per week in office (flexible work-from-anywhere up to 10 days/year). Benefits include 27 days holiday plus 5 additional days off, private medical insurance (Bupa), life insurance, gym membership, mental health support (Spill), and wellness resources.
Requirements
Required:
- Strong experience building and optimizing complex SQL (joins, window functions, optimization)
- Strong grasp of data modelling and warehouse design (Kimball-style dimensional modelling)
- Production dbt experience, including testing and documentation
- Hands-on experience using AI coding tools (Copilot, Cursor, Claude Code) as a real part of your workflow, not occasional use
- Strong fundamentals to work confidently without AI assistance and critically assess AI-generated output
- Familiarity with version control (GitHub)
- Ability to independently translate business logic into technical implementation
- Comfortable giving and receiving code review
Desirable (not required):
- Snowflake experience
- Semantic layer experience (Omni, Cube, dbt Semantic Layer)
- BI tools (Omni, Tableau, Metabase)
- Exposure to AI agent tooling / MCP
- CI/CD for data workflows
- Python/Airflow familiarity
- Terraform / Infrastructure as Code
