Data Engineer

• Technology Stack
   
• • Snowflake
   
• • DBT Cloud or DBT Core
   
• • Azure Data Factory
   
• • SQL Server / T-SQL
   
• • Azure DevOps / Git
   
• • Kimball dimensional modelling
   
• Role Purpose The Senior Data Engineer will support the migration of complex legacy SQL Server stored procedure logic into modular, governed and reusable data platform layers across Bronze, Silver and Gold. This is a hands-on build and delivery role, not a design-only consulting role. Solution design, architecture decisions and modelling standards will be owned by the internal technical lead. The resource is expected to implement, test, validate, document and deliver agreed designs using Snowflake, dbt and Azure Data Factory. The role requires genuine data engineering experience, not report support, dashboard development or light SQL analysis.
   
• Minimum Experience Required
   
• • Minimum 4+ years hands-on data engineering experience.
   
• • Minimum 1+ years Snowflake or modern cloud data warehouse experience.
   
• • Minimum 1+ years dbt Cloud or dbt Core experience.
   
• • Minimum 1+ years Azure Data Factory experience.
   
• • Must have delivered at least one data platform migration or modernisation project
   
• . • Must have migrated or refactored SQL Server stored procedures, or similar legacy stored procedure logic, into modern data platform patterns such as Snowflake, Databricks, Microsoft Fabric or AWS/Azure-native data platforms.
   
• • Must have hands-on experience building facts, dimensions, SCDs, incremental models and batch pipelines.
   
• • Must be comfortable working from incomplete or complex legacy logic and converting it into clean, governed data models.
   

Core Responsibilities

• Analyse complex SQL Server stored procedures and identify embedded business rules.
• Convert procedural SQL logic into reusable Snowflake and dbt models.
• Build Bronze, Silver and Gold transformation layers. • Build Kimball-style facts, dimensions and star schemas. • Implement SCD Type 1 and Type 2 patterns.
• Build incremental, batch and CDC-style processing patterns.
• Build Azure Data Factory ingestion and orchestration pipelines.
• Build dbt models, macros, tests, snapshots, documentation and YAML configuration.
• Support source-to-target mapping, reconciliation and data validation.
• Implement data quality checks and validation rules.
• Support deployment automation, Git and CI/CD processes.
• Maintain mandatory technical documentation covering lineage, transformation rules, reconciliation outcomes, deployment steps and handover notes.