Samhitha Mudiam Email & Phone Number

United States

• Developed and managed various data pipelines using AWS Glue to automate data ingestion, transformation, and loading processes, improving data processing efficiency by 30%.• Engineered robust ETL/ELT processes utilizing AWS Data Pipeline, enhancing data flow and performance, which led to a 30% increase in data processing speed and quality.• Leveraged Amazon Redshift as a data warehouse to consolidate and analyse data from various sources, improving query performance by 20% and reducing storage costs.• Migrated the data aggregation layer from AWS Glue to using Data Build Tool (DBT), resulting in up to 90% cost savings and improved query performance.

Charlotte, North Carolina, United States

• Implemented data streaming solutions using Google Cloud Pub/Sub and Google Cloud Storage to facilitate real-time ingestion and processing of data, enabling timely analytics and insights for academic performance and research projects.• Created HiveQL queries that identified a 30% increase in customer churn rate, allowing for targeted customer retention campaigns.• Integrated Apache Airflow with GCP to monitor multi-stage ML workflows, with tasks running on Google AI Platform and data processing jobs in BigQuery, while contributing to CI/CD solutions using Git and Jenkins for setting up the big data architecture.• Analysed data using Google Dataproc and collaborated with the team to create comprehensive reports and presentations that effectively conveyed key metrics and trends through clear data visualizations in Tableau.• Designed and implemented a scalable data lake architecture to store and manage 100 petabytes of structured and unstructured data, achieving a 50% reduction in storage costs through efficient data tiering.

Bengaluru, Karnataka, India

• Utilized Azure Data Factory to extract data from Azure SQL Database, perform transformations using Azure Databricks notebook, and load the transformed data into Azure Data Lake Storage.• Built and optimized Apache Spark clusters on Azure Databricks to process 500 TB data, accelerating data processing.• Designed and optimized Azure Databricks notebooks with PySpark and Spark SQL to transform data stored in Azure DataLake Gen2 which resulted in a 20% increase in overall system performance• Accomplished interactive Power BI reports leveraging advanced DAX and Power Query modeling techniques to unlock deeper data insights and facilitate informed decision-making.• Optimized Snowflake schema design, achieving a 15% reduction in storage costs while maintaining efficient data retrieval for SQL queries.• Created and deployed Apache Flink applications for processing high-volume streaming data, achieving a 25% improvement in processing speed compared to traditional batch processing methods.• Built data integration pipelines using Informatica PowerCenter, automating data movement and reducing manual effort by 50%.

Master Of Science - Ms, Computer Science

Education record