Bajpaye e
Sr Azure Data Engineer @Wns Global Service PrivateLimited
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WORK HISTORY
Sr Azure Data Engineer @Wns Global Service PrivateLimited
IN
Created and executed technical solutions using several Azure Cloud technical components, including Spark SQL, Pyspark, Azure Data Factory, Data Bricks, and Azure Data Lake storage (ADLS Gen2). Utilize Azure Data Factory, T-SQL, Spark SQL, and Azure Data Lake Analytics to extract, transform, and load data from Sources Systems to Azure Data Storage services and services. Worked on Azure Data Factory, designing and implementing scalable data intake pipelines to consume data from many sources, such as REST APIs, SQL databases, and CSV files. Used Python functions in Azure Functions an ETL procedure was created to move data from AzureCosmos DB to Azure Synapse Analytics according to particular use cases and events. To store and handle massive amounts of data, a scalable data warehouse was designed and created utilising Azure Blob Storage, Data Lake, and Synapse. POWER BI dashboards and visualisations were made in accordance with the specifications. To assist customers and stakeholders in planning future sales and marketing initiatives, Tableau was used to design, build, and automate intelligent business intelligence dashboards. Using Python and Snow SQL, a one-time data migration of multistate level data from SQL server to Snowflake is implemented. Using Power BI\'s DAX expressions code, I carried out data messaging tasks and used the Power BI gateway to update dashboards and reports with data from on-premises sources. SSIS packages were created to import data from several OLTP sources into MS SQL Server. To preserve source code in GitHub and Git repositories, I worked on GIT. Created a number of Jenkins tasks to carry out CI/CD pipelines and continuously integrate the processes. Using Power BI, I created operational, analytical dashboards and data visualization reports.
EDUCATION
University of East London
MBA, Business Analytics
ABOUT BAJPAYE E
With over 4 years of AWS Data Engineering experience, I have strong expertise in designing and developing data ingestion, ETL, and processing pipelines across cloud and on-premises environments. I bring hands-on experience with AWS services including S3, Glue, EMR, Lambda, Redshift, DynamoDB, Athena, Kinesis, DMS, RDS and CloudWatch, ensuring scalable and reliable data solutions. My background includes working extensively with Hadoop ecosystems—HDFS, MapReduce, Hive, Pig, Impala, HBase, Sqoop, Oozie—and building optimized data architectures and pipelines using PySpark, Spark SQL, Kafka, and Glue ETL. I am also experienced in configuring EMR clusters, implementing CI/CD automation with Git and Ansible, and applying dimensional modelling techniques such as Star, Snowflake and SCDs to support analytics and reporting needs.I have strong skills in SQL, PL/SQL, and database object development, coupled with exposure to Azure technologies such as Data Factory, Databricks, HDInsight and Stream Analytics. I have worked extensively with AWS Glue for ETL scripting, PySpark transformations and metadata management through the Glue Data Catalog. Additionally, my experience includes building visual insights using Tableau, Power BI and QuickSight, and managing large-scale data movement using Kafka and Sqoop. With a solid foundation in data warehousing, data mining, automation and cloud-native engineering practices, I consistently deliver robust, high-quality data solutions in fast-paced enterprise environments.
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