Jul 2024 – Present · 1 yr 10 mos

• 🔹 Developed data strategies and led implementation of end-to-end data applications at Paytm.
• 🔹 Managed a team of 10 engineers, achieving a 50% improvement in data processing efficiency.
• 🔹 Saved $1.5 Million annually through innovative data compression techniques.
• 🔹 Engineered a SQL optimizer, enhancing query performance by 50%, empowering Business Analysts
• with improved data retrieval.

Apr 2023 – Jun 2024 · 1 yr 2 mos

• 🔹 Efficiently Migrated data processing workflows from Hive to Starburst, resulting in a 50% enhancement in query performance and a 30% reduction in compute costs.
• 🔹 Developed Python scripts for Starburst POC, performance comparisons, and load testing with Dremio and Hive
• 🔹 Maintained and optimized a feature store using transaction data to calculate customer features for
• lending assessments, reducing job run time from 5 hours to 15 minutes.
• 🔹 Collaborated with Paytm business verticals to understand use cases for analysis and reporting.
• 🔹 Successfully completed a POC for Looker, testing all features.
• 🔹 Migrated Paytm's analysis and reporting system to Looker from Google BigQuery, saving $1 million
• annually.
• 🔹 Contribute in Google Case Study for successful migration from Google Big Query to Looker in a duration of 1 month

Nov 2021 – Mar 2023 · 1 yr 4 mos

• 🔹 Enhanced multi-partition support for ETL jobs at Paytm, boosting parallel processing and query performance by 63% improvement.
• 🔹 Developed a robust data architecture, reducing data latency by 60%.
• 🔹 Developed OLAP solutions processing 1 billion daily rows for Recharge, Utilities, Movies, and Travel verticals, reducing infrastructure costs by 30% and improving data retrieval speeds by 40%.
• 🔹 Reduced Query run time and amount of data by 80% by implementing Data modelling techniques.
• 🔹 Achieved a 40% cost reduction in Google Big Query usage by storing data to cache in Google Big Query Table, saving ~$20K per month.
• 🔹 Collaborated with 50+ cross-functional teams to define data strategies and drive key business decisions.

Nov 2019 – Oct 2021 · 1 yr 11 mos

• 🔹 Migrates 30 PB data from on-premises to AWS and develops the PACE (Paytm Advanced Compute Engine) framework to manage all data processing through the creation of 300 OLAPs and 100 Fact tables for Data analytics and Visualization of entire 55 Paytm Business Verticals for smooth transition from Dahleez(on-premises) to Google BigQuery.
• 🔹 Enabled Star schema design to reduce complexity and improve query performance.
• 🔹 Use clustered and non-clustered indexes on fact and dimension tables to speed up data retrieval.
• 🔹 Partition large fact tables to improve query performance and cube processing.
• 🔹 Define strategic aggregations to balance storage and query performance and Analyze query patterns and create aggregations for frequently accessed data.