Aishwarya Karanth
@aishwaryakaranth
I build scalable enterprise data pipelines for batch and near-real-time pricing platforms.
IndiaWhat I'm looking for
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.
Experience
Work history, roles, and key accomplishments
Architected and led backend data engineering solutions for a configurable Universal Data Editor, enabling batch and near real-time data synchronization. Designed metadata-driven ETL frameworks processing 15M+ records, reducing onboarding effort by 70% and improving pipeline performance by 40%.
Developed and deployed a configurable Go/No-Go Data Validation Framework using SQL, Spark SQL, and PySpark, automating pre-deployment data quality validation. Engineered scalable ETL ingestion pipelines and Power BI dashboards for automated validation reporting.
Education
Degrees, certifications, and relevant coursework
PES University
Bachelor of Engineering, Computer Science
2019 - 2023
Grade: 8.74
Pursued a Bachelor of Engineering in Computer Science, achieving a CGPA of 8.74.
Tech stack
Software and tools used professionally
Availability
Location
IndiaAuthorized to work in
Job categories
Skills
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