Data Engineer
Current• Developed and optimized large-scale data processing workflows using PySpark, resulting in a 40% improvement in processing speed and efficiency for big data analytics projects. Collaborated closely with subject matter experts, end users, and internal and external stakeholders to comprehensively understand project scope and requirements.• Developed reusable ADF pipelines, Databricks notebooks incorporating various ETL&ELT transformations to streamline data processing and enhance reusability across projects.• Created, provisioned different Databricks clusters needed for batch and continuous streaming data processing and installed the required libraries for the clusters.• Utilized Azure Logic Apps to build workflows to schedule and automate batch jobs by integrating apps, ADF pipelines, and other services like HTTP requests, email triggers, etc.• Implemented CI/CD process in new and existing ADF environment while making credentials secure in Key Vault, and deployed production ready ADF resources using pipeline releases.• Responsible for developing ETL&ELT pipelines to meet business use cases by using ADF data flows, Pipelines and Synapses, DataLake, and automate pipelines using various ADF triggers.• Develop customized forms and workflows in PowerApps according to the user requirements.• Create and maintain interactive Power BI visualizations through data interpretation and analyses, integrating various reporting components from multiple data sources.• Configured notifications, alerts, and reminders using Power Automate to keep stakeholders informed about important events, deadlines, and inventory updates.• Automated pipelines deployments and releases using Azure DevOps, Apache Spark, and CI/CD.• Worked with diverse data formats, including XML and JSON, Parquet ensuring compatibility.