Azure Data Engineer with 4.5 years of experience in building scalable data pipelines and analytics solutions on Azure. Skilled in designing ETL workflows using Azure Data Factory and performing large-scale data transformations using Azure Databricks (PySpark, SQL). Experienced in implementing medallion architecture (Bronze, Silver, Gold), incremental data loads, and data quality checks. Proficient in delivering business insights through Power BI dashboards. Strong knowledge of data governance using Unity Catalog and exposure to Microsoft Fabric for unified analytics. Proven ability to deliver end-to-end data solutions in Agile environment
Azure Data Engineer with 4.5 years of experience in building scalable data pipelines and analytics solutions on Azure. Skilled in designing ETL workflows using Azure Data Factory and performing large-scale data transformations using Azure Databricks (PySpark, SQL). Experienced in implementing medallion architecture (Bronze, Silver, Gold), incremental data loads, and data quality checks. Proficient in delivering business insights through Power BI dashboards. Strong knowledge of data governance using Unity Catalog and exposure to Microsoft Fabric for unified analytics. Proven ability to deliver end-to-end data solutions in Agile environment
- Designed and delivered an enterprise Lakehouse solution using Azure Databricks and Delta Lake, integrating data from Oracle, SQL Server, and DB2 into Bronze, Silver, and Gold layers for reporting and analytics.
- Hands-on experience with Microsoft Fabric, including Fabric Lakehouse, One Lake, Dataflows Gen2, and Fabric Pipelines for modern analytics solutions.
- Developed scalable data transformation pipelines using PySpark and Spark SQL, implementing complex business rules, joins, aggregations, and performance optimizations.
- Implemented secure data governance using Unity Catalog, RBAC, and Azure Key Vault to protect sensitive data and ensure compliance requirements.
- Designed and orchestrated Azure Data Factory (ADF) pipelines with dynamic parameterization, automated triggers, and integration across multiple source systems.
- Built near real-time data ingestion pipelines using Databricks Auto Loader for continuous data processing from ADLS Gen2.
- Implemented data quality frameworks, schema validation, reconciliation checks, and audit controls to improve data accuracy and reliability.
- Collaborated with business stakeholders, data analysts, QA teams, and architects in Agile environments to deliver scalable data engineering solutions.