• Led the migration of 10 PB of data, including stored procedures, triggers, and ETLs from Oracle to an S3 Lakehouse architecture, which improved availability and significantly reduced operational costs by 40%.• Developed a customized batch pipeline to reconcile cash flow between BRM systems and the bank, saving 2.5 hours per day of manual effort.• Registrated 50+ tables as clean entities, enhancing data quality and boosting external team confidence in data integrity by 30%.• Optimized AWS costs by 10% by identifying unused tables, optimizing partitioning strategies, and implementing S3 bucket expiration policies.• Managed batch reconciliation processes accommodating over 70 pipelines, ingesting data into more than 30 tables, and processing 2 billion events to reconcile 30TB of data daily.• Optimized the tax pipeline by converting it from full load to incremental load and implementing Spark optimizations, reducing the runtime from an average of 7 hours and 50 minutes to just 1 hour.

• Deployed a comprehensive solution for a major US snack company, aggregating a 360‑degree view of marketing campaign impact throughout its lifecycle.• Built scalable ETL pipelines and architected serverless infrastructure to run Python and Spark jobs.• Implemented solutions for data scientists to deploy ML models in production.• Streamlined data aggregation from over 11 independent sources and automated the ETL process to efficiently roll out the solution.

• Worked with the Social media data team to develop and deliver effective BI solutions to get insight on feeds from different social media platforms.• Developed microservices for handling interactions across various social media platforms, including Facebook, Twitter, and WhatsApp.• Engineered scalable ETL pipelines for social media platforms using PySpark, ensuring efficient data processing and integration.