Analytics Engineer

📋 Description

• Model the business domains you are assigned in dbt Core from their source tables through layered
• Work as the technical counterpart to the Product Analysts who own each business domain: they bring
• Build the tested marts that certified metrics sit on, so a Product Analyst's definition and the
• Prove value parity between each dbt mart and the legacy stored procedure it replaces: row counts
• Own and evolve the dbt layer standards, what belongs in each layer, where collation is normalised
• Declare and test primary keys, and write tests that fail when the model breaks, not checks that

🎯 Requirements

• 3+ years in analytics engineering or data engineering, with production ownership of transformation
• Expert SQL: window functions, CTEs, and multi-entity joins at warehouse scale; strong in the SQL
• Hands-on dbt. You have built and maintained a dbt project (models, layers, refs, tests
• Dimensional modelling judgement: you can state the grain of a model, choose its keys, and place its
• A git-based workflow: pull requests, CI checks, review discipline; plus Python for the tooling
• Working fluency with AI-assisted engineering tooling. You set the task, review the output, and own

🎁 Benefits

• It Would Be Great If You Also Have: Azure PostgreSQL (Elastic Cluster), Microsoft Fabric Warehouse
• A warehouse platform migration you helped carry: engine, dialect, or both.
• Experience migrating stored procedures or warehouse SQL into a transformation framework such as dbt.
• Airflow (or similar) orchestration experience.
• Data-quality, lineage, or parity/audit tooling in your history.
• Standards or tooling you authored that other engineers actually follow.