Analytics Engineer at Multiverse

You will build and maintain the dbt models powering analytics across the business. Reporting to the Director of Data Engineering, you’ll transform raw data into high-quality datasets on Snowflake, ensuring data is accessible and trusted. This is a technical role focused on dimensional modelling, robust CI/CD pipelines, and driving AI adoption through clean data.

• Design and implement scalable data models and warehouse schemas using Kimball-style dimensional modelling techniques within a Snowflake data lake environment.
• Build, test, and document production-grade dbt models to transform complex business logic into clean, accessible datasets for analysts and data scientists.
• Manage the evolution of the data platform by maintaining CI/CD pipelines, performing code reviews, and exposing metrics through a Semantic Layer like Looker or Cube.

• Has 2+ years of experience building and optimising complex SQL and production dbt models, including a strong grasp of window functions and performance tuning.
• Demonstrates deep expertise in data warehouse design and dimensional modelling (fact/dimension tables, SCDs) to support cross-functional analytics requirements.
• Is comfortable working in a modern engineering workflow involving GitHub, CI/CD, and ideally has exposure to Python/Airflow or Snowflake-based architectures.

• Join the UK’s first EdTech unicorn on a mission to equip the global workforce for the AI era, having already driven £2bn+ in ROI for 1,500+ partner companies.
• Work in a high-growth environment backed by $220M in funding, where your data models serve as critical infrastructure for 800+ employees and millions in revenue.
• Benefit from a culture that values engineering excellence, offering a hybrid model, 32+ days of annual leave, and dedicated work-from-anywhere flexibility.