Sesha Sai P
About Sesha Sai P
Sesha Sai P is a Senior Big Data Engineer at Chase, where he has worked since 2022. He has extensive experience in data management and engineering, having previously held positions at PNC and Summa Health.
Work at Chase
Sesha Sai P has been employed at Chase as a Senior Big Data Engineer since 2022. Based in the Columbus, Ohio Metropolitan Area, Sesha focuses on integrating advanced technologies to enhance data processing capabilities. This role involves the application of deep learning models using TensorFlow and Keras within the Azure Machine Learning environment. Sesha's responsibilities include devising Big Data strategies and managing Azure services to optimize data workflows.
Previous Experience at PNC
Prior to joining Chase, Sesha worked at PNC as a Tableau Developer from 2017 to 2018. During this tenure in Pittsburgh, Pennsylvania, Sesha utilized Tableau for data visualization and reporting. Additionally, Sesha employed Snowflake for data warehousing and developed Spark applications using Scala to improve data processing efficiency.
Experience at Summa Health
Sesha served as an AWS Engineer at Summa Health from 2019 to 2021, located in Akron, Ohio. In this role, Sesha engineered systems for regulatory reporting within the financial sector, collaborating with compliance units to ensure adherence to industry standards. This experience contributed to Sesha's expertise in cloud technologies and data management.
Data Management and Governance Expertise
Sesha has executed comprehensive data management strategies, which include crafting data governance frameworks and formulating data lifecycle methodologies. These initiatives are aimed at enhancing data integrity and accessibility within organizations. Sesha's expertise in data governance supports effective data utilization and compliance with regulatory requirements.
Big Data Strategies and Azure Integration
In the role at Chase, Sesha has devised Big Data strategies specifically within the Azure platform. This includes handling the configuration and maintenance of Azure services, directing legacy data migration to Azure Data Lake, and employing Azure Event Grid for effective event management. These efforts are essential for streamlining data operations and improving overall data architecture.