Analytics Engineer

Overview

In this Analytics Engineer role, you will transform raw warehouse data into analytics-ready data marts to support cross-domain reporting and decision-making. You’ll shape modeling standards and the semantic layer while enabling trusted insights through Looker, Power BI and Tableau. The position operates in a hybrid, consumer-facing business with a strong emphasis on data quality and measurable impact. You will work closely with product and commercial teams to modernise the data stack and drive analytics maturity.

Pay / Benefits

• hybrid working
• investment in learning and development
• supportive, collaborative data culture
• impactful, cross-functional projects

Responsibilities

• Design and build Kimball-style dimensional models and gold layer marts using dbt
• Develop and maintain performant, tested dbt pipelines
• Collaborate with Data Engineers to optimise upstream data structures and handoffs
• Implement data quality testing with dbt tests and expectations
• Define and maintain a consistent semantic layer across BI tools
• Translate schemas into business concepts like customers, campaigns, sessions, revenue
• Own documentation including data dictionaries, ERDs, and semantic definitions
• Contribute to migrating legacy warehouses to Snowflake-based models
• Refactor legacy logic into dbt-driven transformations
• Tackle medium-term projects (6–9 months) to align legacy stacks with modern standards

Key requirements

• Strong SQL experience with Redshift or BigQuery; exposure to Snowflake or other modern warehouse
• Proven experience building production dbt models, tests, and environments
• Solid understanding of dimensional modelling and Kimball principles
• Experience with semantic or metric layers
• Python experience for analytics or data engineering tasks
• Experience with BI tools such as Looker and Power BI
• Experience in agile teams using Git-based workflows
• Collaborative mindset
• Attention to data quality and detail
• Proactive communication across cross-functional teams
• SQL (Redshift/BigQuery; Snowflake exposure)
• dbt (models, tests, pipelines)
• Dimensional modelling (Kimball)

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