• ADF and SharePoint Integration: Established connectivity between Azure Data Factory (ADF) and SharePoint using Web API and linked services for streamlined data extraction.• Azure Functions for Data Transformations: Leveraged Azure Functions to perform data transformations effectively, using Python to ensure data accuracy and consistency.• Azure SQL Server: Successfully posted transformed data into Azure SQL Server, serving as a centralized data repository for subsequent analytics in Power BI.• ADLS Data Storage: Utilized Azure Data Lake Storage (ADLS) to store both raw and final data files, ensuring scalable and secure data storage solutions.• Proficiently worked with Amazon Web Services, showcasing expertise in various components:• Leveraged Amazon EC2 instances for versatile computing tasks.• Demonstrated skill in data transformation using AWS Glue.• Orchestrated event-driven functions effectively through AWS Lambda.• Ensured efficient storage management by utilizing Amazon S3 capabilities.

• Creating JDBC URL for SAP AND SQL connections respectively.• Fetching the data from SAP using JDBC connection.• Fetching the data from hyperion servers.• Data Cleaning, Data Processing, and creating end to end Data pipelines.• Automating the process for scheduled runs in crontab. • Monitoring the success/failure emails and performing error handling.• Using postman to debug the payloads in case of errors.• Maintaining log sheets for all the scheduled jobs.

• Installing PI SDK Utility software on the client VM and testing the connections using python.• Building a framework for data extraction at different times of a day from PI server • Data cleaning, processing, and adding the ingestion timestamps through python scripts.• Mapping the tags to their respective IDs in SQL server and loading the data.• Testing the code in UAT • Scheduling the scripts using Windows Task Scheduler • Monitoring the success/failure emails and performing error handling• Maintaining log sheets for all the scheduled jobs

• Creating ABAP and ODBC connections to connect to SAP HANA and SQL Server respectively.• Extracting data from SAP HANA.• Data cleaning and processing through python scripts.• Loading the data in SQL Server.• Testing the code in UAT. • Automating the process for scheduled runs in crontab. • Monitoring the success/failure emails and performing error handling.• Maintaining log sheets for all the scheduled jobs.