Jeethesh R Gs

About Jeethesh R Gs

From designing and implementing over 50 scalable data architectures to optimizing cloud-based pipelines, I have built robust solutions that reduce data latency by up to 40% and improve reporting accuracy by 35%. At AT & T, I orchestrated financial data pipelines using Azure Data Factory and Databricks (PySpark) to integrate multiple revenue and expense sources into a centralized Data Lake (ADLS), significantly boosting financial reporting efficiency. At Johnson & Johnson, I engineered an on-premises data warehouse featuring 15+ ETL pipelines built with HiveQL, Spark, and Presto, which accelerated query execution by 40% and enhanced pharmaceutical inventory management. My technical expertise extends across diverse cloud platforms including AWS, Azure, and GCP. At SonyLiv, I built a highly scalable AWS-based architecture using S3, Redshift, Glue, and Lambda that reduced pipeline latency by 35% and supported real-time content delivery for 50M+ users. Additionally, I have a strong background in SQL coding, data visualization, and statistical analysis, leveraging tools like Power BI Report Builder, Tableau, and DBT to create interactive dashboards and drive strategic decision-making. My proficiency with big data frameworks such as Apache Spark, Hadoop, Hive, and NoSQL databases further equips me to tackle complex data challenges in dynamic industries. Beyond my technical pursuits, I am passionate about continuous learning and innovation. I enjoy following cricket, listening to music that inspires creativity, and traveling to gain fresh perspectives. These interests not only enrich my personal life but also fuel my professional drive, enabling me to approach challenges with renewed energy and a broadened outlook.

Experience

1. Data Engineer

   AT&T

   Jul 2024 — Present

   Orchestrated financial data pipelines using Azure Data Factory (ADF) and Databricks (PySpark) to integrate 10+ revenue and expense data sources into Data Lake(ADLS), enhancing financial reporting efficiency by 20%.• Implemented a Medallion architecture within a Data Mesh framework, enabling scalable, governed data access for enterprise-wide analytics and reducing time-to-insight by 30%.• Developed data quality and validation pipelines to detect anomalies in revenue streams and cost structures, ensuring data integrity and reducing financial reporting discrepancies by 25%.

Education

• University of North Texas

  Master of Science - MS, Data science

• National Institute of Technology Jamshedpur

  Bachelor of Technology - BTech, Electronics and Communications Engineering