Data Engineer

Job Responsibilities

Design and Develop Data Pipelines :

Develop and optimise scalable data pipelines using Microsoft Fabric , including Fabric Notebooks , Dataflows Gen2 , Data Pipelines , and Lakehouse architecture . Work on both batch and real-time ingestion and transformation. Integrate with Azure Data Factory or Fabric-native orchestration for smooth data flow.

Fabric Data Platform Implementation

Collaborate with data architects and engineers to implement governed Lakehouse models in Microsoft Fabric (OneLake) . Ensure data solutions are performant, reusable, and aligned with business needs and compliance standards.

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

Work closely with BI developers, analysts, and data scientists to gather requirements and build high-quality datasets. Support self-service BI initiatives by developing well-structured datasets and semantic models in Fabric.

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

Use your experience with Lakehouses , Notebooks , Data Pipelines , and Direct Lake in Microsoft Fabric to deliver reliable, secure, and efficient data solutions that integrate with Power BI , Azure Synapse , and other Microsoft services.

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