Job Description
Introduction
As an Associate Analytics Engineer, you will play a foundational role in shaping how AI interacts with our data. You will be responsible for building and maintaining a robust semantic layer using dbt and Snowflake, ensuring our data is clean, well-documented, and ready for consumption by both human analysts and AI agents. In doing so, you will help democratise access to key insights across the business, empowering teams at every level to make better, faster decisions without needing to be technical experts.
This is an exciting moment for someone early in their career to help define how the next generation of data tooling is built and used. You will be supported by an experienced team and given real ownership from day one, with a clear path to grow into a more senior analytics engineering role as your skills develop.
Job Responsibilities
• Build and maintain dbt models to create a reliable, well-structured semantic layer on top of Snowflake
• Design and manage data models optimised for use by AI agents and LLM-based tooling (including Optimizely’s own Opal product)
• Develop and maintain metadata, including descriptions, lineage, and business context, to enable AI agents to accurately understand and query the data
• Experiment with AI agents and LLM-based tooling to explore how data models and context layers can power intelligent, autonomous workflows
• Build metrics to track the quality of data, and actively monitor these to provide assurance to business users
• Collaborate with data engineers, analysts, and data scientists to translate business requirements into scalable, reusable data models
• Contribute to documentation standards and best practices across the team
Knowledge and Experience
• At least 1 year of working experience, with knowledge of SQL gained through coursework, projects, or early professional experience
• Exposure to or interest in dbt for data modelling and transformation
• A clear vision for how AI and LLMs can transform the way businesses use and act on data, and a drive to help make that a reality
• Exposure to prompt engineering or LLM-based tooling
• Strong attention to detail, particularly regarding documentation
Education
Bachelor's or Master's degree in Computer Science, Information Systems, Mathematics, Statistics, Physics, Economics, or a related quantitative discipline — or equivalent practical experience.