Analytics Engineer

Overview

As Analytics Engineer, you will build and maintain dbt data models powering analytics and data science across Multiverse. You’ll develop robust, scalable pipelines and ensure data is accessible, trusted, and well-documented, leveraging Snowflake and data-lake architecture. You’ll collaborate with analysts, stakeholders, and product teams to translate requirements into scalable models and a reliable semantic layer. This hands-on role offers ownership, problem solving, and meaningful impact on decision-making across the business.

Pay / Benefits

• 27 days holiday
• life event day and volunteer days
• wellbeing days and bank holidays
• private medical insurance with Bupa
• gym membership & wellness resources
• mental health support via Spill

Responsibilities

• Build and maintain dbt models to transform raw data into clean, documented, accessible datasets
• Translate business and analytics requirements into scalable data models
• Design and implement data warehouse schemas using dimensional modelling (fact and dimension tables, SCDs)
• Participate in design and code reviews to improve model design and query performance
• Expose models and metrics via the Semantic Layer
• Implement and maintain dbt tests to ensure data quality and model accuracy
• Document data models clearly to support cross-functional use
• Use GitHub and CI/CD pipelines to deploy changes safely and efficiently
• Optimize dbt models and SQL queries for performance and maintainability
• Work with Snowflake; develop on top of a data lake architecture
• Ensure models are integrated with data catalogs for downstream use

Key requirements

• 2+ years of building and optimising complex SQL (joins, window functions)
• Strong understanding of data modelling and warehouse design (Kimball-style)
• Experience using dbt in production, including testing and documentation
• Familiar with version control (GitHub)
• Experience tuning dbt models and SQL queries for performance
• Able to independently transform business logic into technical implementation
• Comfortable participating in code reviews
• Desirable (not required): Snowflake; Semantic Layers (Looker, Cube); CI/CD for data workflows; Python/Airflow; data visualization tools (Tableau, Looker); Terraform

• detail-oriented
• solution-driven
• pragmatic
• dbt
• SQL
• Snowflake