Kaustubh Upadhye

Results-driven Data Engineer with 3+ years of experience in designing, developing, and optimizing end-to-end data engineering solutions. Strong expertise in ETL/ELT pipelines, data warehousing, data modeling, SQL, and performance tuning, delivering up to 60% improvement in Big data processing efficiency through automation. Experienced in building scalable, reliable data pipelines to support business intelligence, analytics, and reporting.

Proven ability to collaborate in Agile/Scrum environments, translate business requirements into technical solutions, and deliver high-quality, data-driven outcomes. Passionate about continuous learning and adopting modern data engineering best practices.

Data Engineer at LearningMate (2023-01 – Present)

• Architected, built, and operated scalable, serverless big-data ETL/ELT pipelines using Python, AWS Glue, AWS Lambda, and AWS Step Functions to ingest and process large-volume datasets from REST APIs, relational databases, event streams and flat files into Amazon S3–based data lakes improving pipeline efficiency, and reducing end-to-end processing latency by 40%.
• Optimized SQL/PLSQL queries, views, and stored procedures; applied indexing, partitioning, and query tuning strategies to boost data performance by 60%.
• Designed and implemented medallion architecture and applied SCD Types in data warehouses to manage historical data changes, ensuring accurate data lineage, standardized transformations, and time-based analytics across the data pipeline.
• Contributed to multiple proof-of-concept projects involving data engineering, batch pipelines, and cloud-native architectures that were later adopted in production environments.
• Collaborated with cross-functional teams to troubleshoot and optimize data processes, improving system efficiency.

Post Graduate Diploma in Big Data Analytics – Centre for Development of Advanced Computing (CDAC) (2023-12)

Bachelor's in Mechanical Engineering – SIT (2022-06)