Job description
We’re looking for a Data Engineer to join our growing data team and help build, maintain, and optimise data pipelines that support analytics and reporting across our retail business. You’ll work closely with analytics, BI, and business stakeholders to deliver reliable, well-modelled data that drives decision-making and enables applications of AI such as Natural Language Querying through Databricks Genie.
This is a hands-on role suited to someone with solid foundations in data engineering who is eager to deepen their skills in cloud-based data platforms and modern analytics.
Key Responsibilities
1. Design, build, and maintain scalable data pipelines using Azure Data Factory and Azure Databricks
2. Develop and optimise PySpark and SQL transformations for batch and analytical workloads
3. Model data using dimensional modelling principles (, star and snowflake schemas) to support reporting and analytics
4. Contribute to semantic models and datasets consumed by Power BI, working with measures and DAX where required
5. Ensure data quality, reliability, and performance across data pipelines
6. Support ingestion of data from various data sources and formats
7. Collaborate with BI developers, analysts, and business stakeholders to understand data requirements
8. Monitor, troubleshoot, and improve existing data workflows
9. Follow engineering best practices including version control, documentation, and testing
Required Skills & Experience
10. 1–4 years of experience in a Data Engineer, Analytics Engineer, or similar role
11. Hands-on experience with Azure Databricks and Azure Data Factory
12. Strong working knowledge of SQL
13. Experience using PySpark for data transformations
14. Familiarity with Power BI, including datasets and basic to intermediate DAX
15. Solid understanding of dimensional data modelling for analytics and reporting
16. Experience working with large datasets in a cloud environment
17. Understanding of data quality, performance tuning, and pipeline orchestration concepts.
Desirable
18. Experience in a retail or consumer-focused industry
19. Exposure to Data Lake or Lakehouse architectures
20. Familiarity with CI/CD concepts for data pipelines
21. Experience working with incremental loads and slowly changing dimensions (SCDs)
22. Knowledge of Azure services beyond the core stack (, Azure Storage, Azure SQL)
23. Understanding of security principles beyond RLS and Data Access (Networking, MFA, RBAC)