Narayana Murthy Email & Phone Number

VA

• Established and maintained an ETL pipeline using Informatica PowerCenter to extract, transform, and load data from multiple sources into a data warehouse, ensuring data accuracy and consistency.
• Accomplished a complex data processing workflow consisting of multiple stages (data extraction, transformation, loading) using AWS Pipeline, improving data pipeline reliability and 40% reduction in data processing.
• Optimized Lambda functions for cost efficiency, achieving a 20% reduction in execution costs through code refactoring and best practices.
• Migrated data aggregation layer from legacy services to Snowflake unitizing Data Build Tool (DBT) models resulting in up to 70% cost savings and improved query performance.
• Orchestrated complex data pipelines with 3 stages using AWS Step Functions to automate data movement and transformation tasks, ensuring reliable data flow.
• Implemented a data warehouse on Databricks using Delta Lake, improving data query performance by 40% compared to the previous Teradata-based solution.

India

• Integrated Apache Airflow with AWS to monitor multi-stage ML workflows, with tasks running on Amazon SageMaker, and contributed to CI/CD solutions using Git and Jenkins for setting up and configuring the big data.
• Created and managed 5+ ETL pipelines using AWS Glue, automating data extraction, transformation, and loading from various sources to a data warehouse.
• Built robust data quality checks and validation rules to ensure data integrity and accuracy throughout the Data pipeline process, reducing data errors by 50% and improving data reliability.
• Leveraged SQL and Alteryx to streamline data manipulation processes, resulting in a 20% improvement in data quality and a 30% reduction in processing times.
• Streamlined data ingestion and transformation processes by orchestrating multiple tasks and dependencies within Airflow DAGs.
• Enhanced AWS Redshift clusters for cost efficiency, achieving a 30% reduction in cluster costs, and maintaining query performance.

India

• Executed ad-hoc queries and generated insights on petabyte-scale datasets using AWS Athena, providing real-time analytics to business stakeholders.
• Utilized Snowflake's query optimization features (materialized views, indexing) to improve query performance by 30% and reduce query execution time.
• Developed and optimized Apache Spark jobs for large-scale data processing tasks, including filtering, aggregation, and transformations using Spark SQL, DataFrames, and RDDs.
• Implemented checkpointing and state management strategies in Flink to ensure data integrity and recovery from system failures during real-time processing.
• Performed data sharing and collaboration using Nessie's branching and merging features, facilitating cross-functional data analysis.
• Employed ETL pipelines using Hive to extract, transform, and load data from various sources, increasing data ingestion rates by 25%.

Master'S Degree, Data Science

Bachelor'S Degree, Electrical, Electronics And Communications Engineering