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.