LOKESHH KUMAR GURUSAMY RAJU

I currently work as a Senior Data Engineer in Texas Capital Bank. My responsibilities are as follows. 

• Design and Develop Python ETL Process script in AWS Glue to migrate Campaign data from external sources like S3, Parquet/Text Files into Snowflake. Design and develop ETL jobs using IBM DataStage or Apache Nifi. These tools allow you to extract data from various sources, transform it according to business rules or data requirements, and load it into a central repository like Snowflake. 

• Built AZURE pipelines from scratch which involved creating AWS workflows which in-turn creating glue jobs with its schedule. 

• a cloud-based data warehousing platform, to create and manage the central repository. Define appropriate data schemas, tables, and storage structures to accommodate the data requirements and optimize query performance. 

• I Lead a team of 7 People both offshore and onsite. I Assign tasks and evaluate their performance. 

• Used python to make transformations in the raw file and then convert it into parquet. 

• Create external tables with partitions using Snowflake. 

• Write SQL queries and views in Snowflake to support reporting and visualization outcomes. These views provide a simplified and aggregated representation of the underlying data, making it easier for business users or reporting tools to consume and analyze the data. 

• Created Data ingestion framework in Snowflake for both Batch and Real-time Data from different file formats 

(CSV, JSON, PARQUET) using Snowflake Stage and Snowflake Data Pipe 

• Implemented Spark using Python and Utilizing Data Frames and Spark SQL for faster processing of data. 

• Architect and develop cross-design AWS Solutions using Lambda, EMR, SNS, DYNAMO & EC2 

• Create a Lambda function to run the AWS Glue job based on the AWS S3 event. 

• Created monitors, alarms, notifications and logs for Lambda functions, Glue Jobs using CloudWatch. 

• Explored data in a variety of ways and across multiple visualizations using Power BI. 

• Optimize ETL jobs, SQL queries, and data pipelines for efficient data processing and query performance. Identify and address bottlenecks, improve data loading speeds, and tune SQL queries to enhance overall system performance. 

• Hands-on – AWS GLUE, WORKFLOW, S3, IAM, ATHENA, Secrets Manager, Sage Maker, CloudWatch, Snowflake, MuleSoft, PowerBi, MOVEit, Azure, Python. 

• Design and Develop Python ETL Process script in AWS Glue to migrate Campaign data from external sources like S3, Parquet/Text Files into Snowflake. Design and develop ETL jobs using IBM DataStage or Apache Nifi. These tools allow you to extract data from various sources, transform it according to business rules or data requirements, and load it into a central repository like Snowflake. 

• Built AZURE pipelines from scratch which involved creating AWS workflows which in-turn creating glue jobs with its schedule. 

• a cloud-based data warehousing platform, to create and manage the central repository. Define appropriate data schemas, tables, and storage structures to accommodate the data requirements and optimize query performance. 

• I Lead a team of 7 People both offshore and onsite. I Assign tasks and evaluate their performance. 

• Used python to make transformations in the raw file and then convert it into parquet. 

• Create external tables with partitions using Snowflake. 

• Write SQL queries and views in Snowflake to support reporting and visualization outcomes. These views provide a simplified and aggregated representation of the underlying data, making it easier for business users or reporting tools to consume and analyze the data. 

• Created Data ingestion framework in Snowflake for both Batch and Real-time Data from different file formats 

(CSV, JSON, PARQUET) using Snowflake Stage and Snowflake Data Pipe 

• Implemented Spark using Python and Utilizing Data Frames and Spark SQL for faster processing of data. 

• Architect and develop cross-design AWS Solutions using Lambda, EMR, SNS, DYNAMO & EC2 

• Create a Lambda function to run the AWS Glue job based on the AWS S3 event. 

• Created monitors, alarms, notifications and logs for Lambda functions, Glue Jobs using CloudWatch. 

• Explored data in a variety of ways and across multiple visualizations using Power BI. 

• Optimize ETL jobs, SQL queries, and data pipelines for efficient data processing and query performance. Identify and address bottlenecks, improve data loading speeds, and tune SQL queries to enhance overall system performance. 

• Hands-on – AWS GLUE, WORKFLOW, S3, IAM, ATHENA, Secrets Manager, Sage Maker, CloudWatch, Snowflake, MuleSoft, PowerBi, MOVEit, Azure, Python. 

 University of Massachusetts - Dartmouth 12/2020 

Master of Science: Computer and Information Sciences 

Related Courses : Advanced Machine Learning, Software Development, Algorithms and Complexity, Human Computer Interaction, Advanced Computer Networks, Advanced Computer Systems, Theoretical Computer Science.,