Srikanth K
About Srikanth K
Srikanth K is a Senior Data Engineer at Travelport, where he has worked since 2022. He specializes in utilizing Amazon Web Services for data management and has extensive experience in designing ETL processes and implementing real-time data solutions.
Work at Travelport
Currently, Srikanth K serves as a Senior Data Engineer at Travelport, a role he has held since 2022. Based in Englewood, Colorado, he focuses on leveraging cloud technologies and data engineering practices to enhance data processing capabilities. His responsibilities include designing and developing ETL processes, as well as managing data integration tasks that support various applications within the organization.
Education and Expertise
Srikanth K studied at Srtist, where he gained foundational knowledge in data engineering and related fields. His expertise encompasses a broad range of technologies, including Amazon Web Services (AWS), Informatica, and Spark-Streaming APIs. He has developed a strong skill set in data migration, integration, and cloud data management, positioning him as a knowledgeable professional in the data engineering landscape.
Technical Skills and Tools
Srikanth K has extensive experience in utilizing Amazon Web Services, particularly Linux/Ubuntu, to launch and configure Amazon EC2 Cloud Instances for various applications. He employs AWS Athena to import structured data from S3 into systems like RedShift for report generation. His proficiency also extends to designing ETL processes using Informatica 10.4, handling data from diverse sources such as Oracle, flat files, Salesforce, and AWS cloud.
Data Migration and Integration Projects
In his role, Srikanth K has successfully executed data migration projects, including moving data from Snowflake to S3 for TMCOMP/ESD feeds. He has developed Snowflake views to facilitate efficient data loading and unloading between AWS S3 buckets. Additionally, he has implemented Spark-Streaming APIs to create a common learner data model by processing data from Kinesis in near real-time.