Manideep Chittiprolu Email & Phone Number

United States

• Led the end-to-end migration of large-scale data of finance from on-premises systems to AWS cloud infrastructure, ensuring minimal downtime and data integrity.
• Designed and developed scalable and robust data pipelines using AWS Glue, PySpark, and AWS EMR, resulting in improved data processing efficiency and reliability.
• Orchestrated complex ETL workflows with Apache Airflow, automating data extraction, transformation, and loading processes to ensure data accuracy and consistency.
• Managed data warehousing solutions with AWS Redshift and Snowflake, optimizing schema design and query performance for faster data retrieval and analysis.
• Implemented partitioning, indexing, and other performance tuning techniques to enhance data query efficiency.
• Administered Oracle Database and Amazon DynamoDB, ensuring high availability, scalability, and data security.

Greensboro, North Carolina, United States

• Processed over 10 TB of university’s data using Azure services including Azure Data Lake Storage and Azure Cosmos DB, resulting in improved data handling efficiency and scalability.
• Developed and validated advanced financial models that accurately projected financial outcomes with a forecast accuracy rate of over 95%, leading to informed decision-making and strategic planning.
• Orchestrated and automated data workflows for 10+ heterogeneous data sources using Azure Data Factory pipelines and Databricks clusters, reducing data processing time by 50%.
• Implemented machine learning algorithms and models for various tasks, resulting in a 30% improvement in model performance and scalability compared to previous implementations.
• Contributed domain-specific knowledge and technical expertise to interdisciplinary teams, leading to the successful completion of 5 research projects and the development of innovative analytical methodologies.
• Conducted ETL processes to extract, transform, and load university data using Talend, ensuring data accuracy, consistency, and Hadoop for processing and analyzing large volumes of university data.

Hyderabad, Telangana, India

• Designed and deployed AWS-based ETL pipelines for 10+ diverse medical device data sources, facilitating the ingestion and processing of over 50 TB of medical records data daily.
• Engineered scalable data transformation processes using AWS Lambda, Apache Spark, SQL, Databricks and AWS Glue resulting in a 40% improvement in data quality and integrity.
• Implemented advanced data modeling techniques, resulting in a 40% improvement in query performance for multidimensional medical device datasets, enabling efficient data retrieval system for stakeholders.
• Utilized AWS Redshift and Athena analyze over 100 TB of medical device data, enabling stakeholders to uncover actionable insights and trends with 15% increase in operational efficiency.
• Orchestrated automated deployment and scaling of data processing infrastructure, reducing deployment time by 60% and enabling seamless scalability to accommodate fluctuating data volumes.
• Implemented robust data governance and security measures, resulting in a 30% reduction in data security incidents and ensuring compliance with industry regulations such as HIPAA and GDPR.

Master Of Science - Ms, Computer Science

Bachelor Of Technology - Btech, Electrical, Electronics And Communications Engineering