Designed and maintained scalable data pipelines to support advanced analytics and reporting solutions 

Developed and optimized data models using Azure Synapse and Databricks to ensure efficient data consumption across business units 

Collaborated closely with data scientists and business analysts to integrate machine learning models into production workflows 

Built robust ETL/ELT processes using Azure Data Factory, PySpark, and SQL for ingesting structured and unstructured data 

Ensured data quality and consistency by implementing validation and monitoring frameworks 

Developed batch and streaming data pipelines using Azure Data Factory and Databricks 

Wrote performant SQL and PySpark scripts for processing large datasets in Azure and AWS environments 

Led IBM DataStage migration and performance tuning initiatives 

Automated data ingestion pipelines and implemented monitoring using Control-M

Built batch and streaming ETL pipelines using Azure Data Factory and Databricks 

Integrated IoT data into Synapse Analytics and visualized insights using Power BI 

Monitored and optimized pipeline performance and reliability

Led automation of business processes using SAS, SQL, and Excel 

Built interactive dashboards in Power BI for KPIs and stakeholder reporting 

Conducted business performance analysis and optimized server environments 

Developed ETL jobs using IBM InfoSphere DataStage 

Created SQL scripts and shell scripts in UNIX for data integration 

Participated in PL/SQL development and performance tuning

Built ETL jobs using IBM InfoSphere DataStage 

Developed scripts for Teradata and UNIX automation 

Created Control-M schedules and executed unit testing