• Design and develop batch and streaming data pipelines using Azure Data Factory, Fabric Data Factory, notebooks and related services.
• Build curated lakehouse, warehouse and semantic data layers that support analytics and AI use cases.
• Implement data-quality checks, metadata, lineage, monitoring, alerting and operational support.
• Optimise pipelines and storage for reliability, security, performance and cost.
• Apply source control, automated testing and CI/CD through Azure DevOps or GitHub.
• Own delivery of your workstreams end-to-end — from design through to production, managing risk and stakeholders along the way
• Set the bar for engineering quality — reviewing designs, shaping standards, and mentoring the team to raise the floor, not just the ceiling.

Qualification

Qualifications, skills and experience:

• Significant data-engineering experience with SQL and Python or PySpark.
• Hands-on experience with Microsoft Fabric or Azure services such as Data Factory, Data Lake Storage, Synapse Analytics and Databricks.
• Knowledge of dimensional modelling, lakehouse patterns, medallion architecture and data integration.
• Experience with identity, access control, data protection and production support.
• Demonstrated ability to own technical workstreams and communicate design decisions clearly to both technical and non-technical audiences.
•