Rithvik P Email & Phone Number

• Played a key role in a data migration initiative from AWS to Google Cloud Platform (GCP), contributing to the successful transition of data infrastructure.• Developed and implemented a detailed migration strategy to transfer data from AWS S3 and Redshift to Google Cloud Storage (GCS) and Big Query.• Contributed to the development and implementation of data transformation processes, converting AWS-based data into Big Query-compatible formats to ensure data compatibility and accessibility.• Transformed and optimized SQL queries from AWS Redshift to Big Query SQL, improving performance and reducing costs.• Participated in the extraction, transformation, and loading (ETL) process, ensuring smooth migration of source tables while maintaining data integrity throughout.• Collaborated in utilizing Info Works Replicator to replicate data from AWS source systems to Google Cloud Storage, facilitating reliable and scalable data transfer.• Identified opportunities for optimizing the data pipeline, proposing solutions to enhance the efficiency of data processing and reduce latency.• Developed scripts and automated processes for data synchronization between MongoDB and SQL databases during the migration, ensuring consistent and up-to-date data across systems.• Optimized query performance and resource allocation in Big Query by partitioning tables and applying best practices for data modeling.• Leveraged PySpark to process and transform large datasets, ensuring efficient handling of data throughout the migration pipeline and maintaining data quality.• Improved Big Query SQL queries using advanced techniques, resulting in significant performance gains and faster query execution times.

Played a key role in migrating on-premise ETL processes to Google Cloud Platform (GCP), utilizing cloud-native tools such as Big Query, Cloud DataProc, Google Cloud Storage, and Composer to streamline data workflows.

• Assisted in designing and implementing data pipelines to support Amazon’s Last Mile Delivery Service Partner (DSP) initiatives, contributing to the overall improvement of driver safety and operational efficiency.• Worked closely with senior engineers and business stakeholders to gather requirements and develop scalable data solutions, ensuring alignment with business goals.• Developed and maintained ETL processes using AWS services such as Redshift, S3, and Glue to manage data flows across multiple production systems, improving data processing reliability.• Collaborated on the migration of legacy data pipelines to AWS cloud architecture, ensuring scalable and cost-efficient solutions while reducing processing time by 20%.• Gained experience in working with both relational (Redshift, Oracle) and non-relational databases (NoSQL), contributing to efficient data modeling and data storage strategies.• Wrote Python and PowerShell scripts to automate data workflows, reducing manual processes by 30% and improving the overall efficiency of pipeline management.• Provided troubleshooting support for data pipeline issues, helping to resolve operational challenges, and ensuring consistent data flow.• Contributed to team-wide knowledge sharing and participated in discussions to explore new technologies and improve existing data systems.• Key Contributions:• Assisted in reducing pipeline processing times by 20%, improving data availability for real-time decision-making.• Gained hands-on experience in AWS technologies and cloud-based data management, contributing to a more efficient and reliable data infrastructure.• Automated manual data processes, reducing human intervention by 30%.

• Transformed customer, product, and sales data from text files, relational databases, log files into interactive dashboards to enable data driven decisions.

Master'S Degree, Engineering/Industrial Management

Bachelor Of Technology - Btech, Engineering

Certified Architect, Data Science, Machine Learning And Neural Networks

Certified Architect, Data Science, Machine Learning And Neural Networks