Aishwarya Karanth

At McKinsey & Company, I architect backend data engineering solutions for a configurable Universal Data Editor, enabling batch and near-real-time synchronization across eight enterprise pricing and clearance use cases. I've built metadata-driven ETL frameworks processing 15M+ records, cutting client onboarding effort by 70%. I’ve also engineered 10+ production ETL and pricing optimization pipelines, improving execution performance by 40% and reducing compute costs by 60% through Spark, partitioning, query optimization, and Delta Lake optimization. I automate data quality validation, Airflow orchestration, CI/CD, testing, infrastructure provisioning, and GenAI-powered documentation. My work spans Kafka, Azure Databricks, Snowflake, Unity Catalog, Hive Metastore, and production support for highly available enterprise data platforms.