Ragavendiran Murthy
Senior Data Engineer @Intuita
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WORK HISTORY
Senior Data Engineer @Intuita
Newbury, GB
EDUCATION
Dr. C. V. Raman University, Kota Bilaspur
Master's degree, Information Technology
Thiruvalluvar University, Vellore
Bachelor's degree, Computer Science
St. Joseph's Matriculation Higher Secondary School
High School Diploma, Computer Science
ABOUT RAGAVENDIRAN MURTHY
Senior Data Engineer with 10 years of IT experience, specializing in data engineering, architecture, and migration on GCP, AWS, and other platforms. •Proven expertise in designing scalable data pipelines, modernizing workflows, and optimizing data integration processes to drive business insights. •Skilled in SQL, Python, DBT, and cloud technologies, with a focus on automation, cost-efficiency, and scalability. •Experience in different technologies like GCP, AWS, DBT, Hadoop Hive, Kafka, Oracle, MySQL, Unix, Informatica, SAP B1. •6 years of experience with GCP BigQuery, Data Flow, DataStream, Cloud Storage, Pub/Sub, Data Fusion, Cloud SQL, Cloud Composer, Cloud Spanner, Cloud IAM & Monitoring, Looker, Looker Studio, DBT, Terraform, Python. •5 years of experience with AWS services including EC2, Lambda, IAM, S3, RDS, DynamoDB, Glue, EMR, Redshift, Athena, Kinesis, and DMS. Designed and implemented secure, scalable, and cost-efficient solutions for data engineering and migration. •Collaborated directly with Google and AWS teams on multiple projects involving database migrations for their clients. Conducted workshops, Proof of Concepts (POCs), and end-to-end implementation of migration solutions tailored to customer needs. •Utilized Python to implement custom scripts and automation for migration processes on both GCP and AWS. •Built data pipelines from On-prem to GCP cloud – (MySQL-Cloud Storage-Pub/Sub-Data Flow-BigQuery) and to AWS cloud – (On-premises data sources-S3-Glue-Redshift), ensuring robust and scalable workflows. •Experience in using Cloud Composer, Apache Airflow, and AWS Glue for job automation and orchestration. •Built modular, reusable DBT models leveraging Jinja macros to enable efficient SQL development and reduced data redundancy, supporting both GCP and AWS environments.
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