Data Engineer

Job Responsibilities

Design and Develop Data Pipelines:

Microsoft Fabric

Fabric Data Platform Implementation

Data Pipeline Optimisation

Monitor and improve performance of data pipelines and notebooks in Microsoft Fabric. Apply tuning strategies to reduce costs, improve scalability, and ensure reliable data delivery across domains.

Collaboration With Cross-functional Teams

self-service BI

Documentation And Reusability

Document pipeline logic, lakehouse architecture, and semantic layers clearly. Follow development standards and contribute to internal best practices for Microsoft Fabric-based solutions.

Microsoft Fabric Platform Execution

Lakehouses

Required Skills And Qualifications

• 5+ years of experience in data engineering within the Azure ecosystem, with relevant hands-on experience in Microsoft Fabric, including Lakehouse, Dataflows Gen2, and Data Pipelines.
• Proficiency in building and orchestrating pipelines with Azure Data Factory and/or Microsoft Fabric Dataflows Gen2.
• Solid experience with data ingestion, ELT/ETL development, and data transformation across structured and semi-structured sources.
• Strong understanding of OneLake architecture and modern data lakehouse patterns.
• Strong command of SQL,Pyspark, Python applied to both data integration and analytical workloads.
• Ability to collaborate with cross-functional teams and translate data requirements into scalable engineering solutions.
• Experience in optimising pipelines and managing compute resources for cost-effective data processing in Azure/Fabric.

Preferred Skills

• Experience working in the Microsoft Fabric ecosystem, including Direct Lake, BI integration, and Fabric-native orchestration features.
• Familiarity with OneLake, Delta Lake, and Lakehouse principles in the context of Microsoft’s modern data platform.
• expert knowledge of PySpark, strong SQL, and Python scripting within Microsoft Fabric or Databricks notebooks.
• Understanding of Microsoft Purview or Unity Catalog, or Fabric-native tools for metadata, lineage, and access control.
• Exposure to DevOps practices for Fabric and Power BI, including Git integration, deployment pipelines, and workspace governance.
• Knowledge of Azure Databricks for Spark-based transformations and Delta Lake pipelines is a plus.