Analytics Engineer
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Requirements
• What you’ll work on • Data Modelling & Transformation • Build and maintain dbt models to transform raw data into clean, documented, and accessible data sets • Translate business and analytics requirements into scalable data models • Design and implement data warehouse schemas using dimensional modelling techniques (fact and dimension tables, slowly changing dimensions, etc.) • Participate in design and code reviews to improve model design and query performance • Expose these models and associated metrics via our Semantic Layer • Testing, Documentation, and CI/CD • 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 manage code and deploy changes safely and efficiently • Performance & Architecture • Optimise dbt models and SQL queries for performance and maintainability • Work with Snowflake; developing on top of a data lake architecture • Ensure dbt models are well-integrated with data catalogs and accessible for downstream use • 2+ years of building and optimising complex SQL (including complex joins, window functions and optimisation methods) • Strong understanding of data modelling and warehouse design (e.g., Kimball-style dimensional modelling) • Experience using dbt in production environments, 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 and contributing to code reviews • Experience with Semantic Layers (e.g. Looker, Cube etc.) • Experience with CI/CD for data workflows • Familiarity with Python/Airflow for data transformation or orchestration tasks • Experience with data visualisation tools (e.g., Tableau, Looker) • Working knowledge of infrastructure-as-code tools like Terraform
Responsibilities
• Build and maintain dbt models to transform raw data into clean, documented, and accessible datasets. • Translate business and analytics requirements into scalable data models using dimensional modelling techniques. • Implement and maintain dbt tests for ensuring data quality and model accuracy. • Document data models clearly to support cross-functional use. • Optimize dbt models and SQL queries for performance and maintainability, working with Snowflake on a data lake architecture. • Ensure well-integrated dbt models are accessible through our Semantic Layer.
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