Analytics Engineer

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 June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post-money valuation of $1.7bn, the round makes us the UK’s first EdTech 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 focus on developing robust and scalable dbt pipelines and contributing to the evolution of our data platform, ensuring that data is accessible, trusted, and well-structured. This role is hands-on and ideal for someone with a strong technical foundation who enjoys solving data problems, writing clean and efficient SQL, and collaborating with analysts, business stakeholders and product teams. This role sits within the Data & Insight team, reporting to the Director of Data Engineering. We’re looking for someone who’s detail-oriented, solution-driven, and pragmatic - someone who takes ownership of their work and is excited to build robust, maintainable data models while being responsive to our users’ needs. 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 WHAT WE’RE LOOKING FOR Required Skills & Experience - 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 Desirable - but not required - Experience with Snowflake - 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 Benefits - Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company