Junior Azure Data Engineer

About the job

Role PurposeThe purpose of this role is to support the development and maintenance of enterprise data solutions, including the Data Lakehouse, Data Warehouse, and Medallion Architecture.This role will work alongside senior engineers and architects to build, test, and deploy scalable data pipelines, with a focus on tasks such as ingestion, data validation, and structured transformations.The role is a hands-on opportunity for someone early in their data engineering career to gain deep experience with Microsofts Azure Data Platform and Microsoft Fabric, working on real-world solutions under guidance and mentorship. Key ResponsibilitiesData Integration Support

• Assist with building and maintaining data ingestion pipelines from various sources using tools like Microsoft Fabric, Azure Data Factory, and Synapse pipelines.ETL Development
• Develop and maintain reusable ETL processes, supporting the Bronze, Silver, and Gold layers of the Medallion Architecture, with a focus on reliability, reusability, and simplicity.Notebook Automation Support
• Help implement and schedule metadata-driven notebooks in Fabric, working with Spark under the supervision of senior engineers.Data Quality and Testing
• Assist in implementing and monitoring data quality checks, validation rules, and basic lineage tracking.Documentation and Data Catalogue Support
• Document data flows, transformations, and support updates to data cataloguing tools or metadata repositories.BI & Reporting Integration
• Assist with connecting data models to Power BI, and help develop certified datasets and semantic layers for self-service users.Environment & DevOps Support
• Learn and contribute to CI/CD processes using Azure DevOps and Git. Support code reviews and testing cycles.Learning and Development
• Be an active participant in code reviews, design sessions, and platform learning. Take ownership of tasks and work through feedback constructively.Testing and Data Quality Assurance
• Develop and maintain unit tests to validate pipeline logic and data transformations. Collaborate with engineering peers to continuously refine test coverage and uphold data integrity standards.

Performance IndicatorsSuccess is measured by:

• Successful delivery of sprints to the satisfaction of stakeholders.
• Data pipeline reliability: pipelines run with minimal failures and auto-recovery.
• Data latency: freshness meets defined SLAs for Bronze, Silver, and Gold layers.
• Valuable insight into the business is delivered through the implementation of new solutions.
• Innovation: regular adoption of new Fabric, AI, and automation features.