Senior Azure data Engineer-Immediate

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT solutions on Azure.
• Build and manage data solutions using Microsoft Fabric, including:
• Fabric Lakehouse
• Fabric Warehouse
• Data pipelines and related Fabric components
• Develop and optimize data processing solutions using PySpark.
• Design and implement data integration workflows using Azure Data Factory (ADF).
• Work with Azure Synapse Analytics for data warehousing, analytics, and large-scale data processing.
• Develop and maintain solutions using Azure SQL.
• Design and implement dimensional data models, including fact and dimension tables, star/snowflake schemas, and slowly changing dimensions.
• Develop reusable and scalable data transformation frameworks.
• Implement CI/CD pipelines for data engineering solutions using Azure DevOps or similar tools.
• Apply data engineering best practices around data quality, security, governance, lineage, and access control.
• Troubleshoot data pipeline failures and production issues and provide root-cause analysis.
• Perform query, pipeline, storage, and compute performance optimization.
• Work closely with data architects, analysts, application teams, and business stakeholders to understand data requirements.
• Participate in code reviews, technical design discussions, and documentation.

Required Skills

• 610 years of experience in Data Engineering.
• Strong hands-on experience with Microsoft Azure data services.
• Strong experience with Microsoft Fabric.
• Hands-on experience with:
• Fabric Lakehouse
• Fabric Warehouse
• Azure Synapse Analytics
• Azure Data Factory (ADF)
• Azure SQL
• Strong programming experience with Python and PySpark.
• Good understanding of SQL and complex query development.
• Strong knowledge of Dimensional Modeling and data warehouse concepts.
• Experience with ETL/ELT architecture and data pipeline development.
• Experience implementing CI/CD for data engineering projects.
• Understanding of Data Governance, Data Quality, Security, and Data Lineage.
• Strong understanding of performance optimization techniques for data pipelines, SQL queries, Spark jobs, and data warehouses.
• Experience working with large datasets and distributed data processing.