<p>Designed and maintained 20 ADF pipelines, reducing manual data ingestion effort by 15 hours per week and improving data availability for reporting by 40%.</p><p> Ingested data from 4 enterprise sources ADLS Gen2, REST APIs, SFTP and JSON, processing approximately 1 million records daily.</p><p> Developed metadata-driven ADF pipelines using Lookup, Switch, Execute Pipeline, and Stored Procedure activities for dynamic and reusable workflow execution.</p><p> Migrated over 100GB of HR and Operations data from legacy systems, Azure SQL Database, and file-based sources into Azure Data Lake Storage Gen2 using Azure Data Factory as part of the Talent Data Lake and Operations Data Lake initiatives.</p><p> Developed scalable data transformation and aggregation solutions using Azure Databricks, PySpark, and Spark SQL to process large volumes of structured and semi-structured data.</p><p> Configured Azure Monitor alerts and ADF pipeline monitoring for proactive issue detection and operational visibility.</p><p> Optimized SQL queries using indexing, query refactoring, and execution plan analysis, accelerating data retrieval by 20%.</p><p> Designed and deployed 5 SSRS reports for business intelligence dashboards, reducing manual reporting time by 15%.</p><p> Built PySpark transformation notebooks in Azure Databricks following the Medallion architecture (Bronze, Silver, Gold).</p><p> Managed source code, branching strategy, and deployment pipelines using Azure DevOps with CI/CD best practices.</p><p> Implemented SCD Type 1 & Type 2 transformations using PySpark, Spark SQL, and Delta Lake for structured and semi-structured datasets.</p><p> Implemented role-based access control across development, QA, and production environments using Azure RBAC to ensure secure and controlled resource access.</p><p> Represented the support team in Global Outage Management calls, communicating production issues, root cause findings, and preventive action plans.</p>
