Senior Analytics Engineer

Description:

A global, privately-owned media group with brands spanning across publishing, audio, and out-of-home advertising, is seeking an Analytics Engineer to join their growing data team.

As the business matures its data capabilities, they are evolving toward a clearer separation between Data Engineering, Analytics Engineering, and Data Product disciplines. This role will sit firmly in the Analytics Engineering function, focused on modelling and building the semantic layer that powers consistent, reliable insights across the company’s BI and data science platforms.

This role will focus on the “middle layer", designing dimensional models, building reusable dbt pipelines, and ensuring the semantic layer meets the needs of Power BI, Looker, and data science teams. There’s also the opportunity to stretch into data product work, partnering with stakeholders to understand and translate business requirements into robust data models.

Key Responsibilities:

• Build and maintain well-structured, scalable data models using dbt and Kimball/Medallion architecture.
• Develop and own the semantic layer, shifting calculations and business logic out of BI tools and into the model layer to drive consistency across Looker, Power BI, and other downstream consumers.
• Work closely with Data Engineers responsible for ingestion (from source systems to raw layers such as S3 or cloud storage), but focus your efforts on the modelling and transformation stage.
• Collaborate with the Data Product team to ensure the semantic layer serves evolving business and analytical needs.
• Support best practices in CI/CD (using GitHub) for productionising and maintaining dbt pipelines.
• Contribute to a common, reusable data model that serves BI, Data Science, and AI/ML teams alike.

Required Skills & Experience:

• Strong experience with SQL and dimensional modelling in dbt.
• Proven experience building and maintaining semantic layers in modern data platforms.
• Familiarity with Medallion architecture, CI/CD processes (GitHub), and version-controlled data workflows.
• BI visualisation experience, such as Looker or Power BI preferred.
• Understanding of data ingestion and orchestration tools (e.g., Airflow, Terraform) is also beneficial.
• Exposure to streaming or log data handling would also be advantageous, though not required.
• Strong communication skills to engage with Data Product stakeholders and translate business needs into technical solutions.