Legacy ETL to Lakehouse Migration
Production-grade migration of 20+ AWS Glue ETL workflows from Informatica/Glue to Databricks Lakehouse for Marsh McLennan.
Kolkata, IndiaBuilding in public since Jul 2016 (~10.1 yrs)
Across five consulting-style roles, you've repeatedly led migrations from legacy ETL and Hadoop systems onto modern Lakehouse platforms - a specialization in transformation, not just maintenance.
A timeline of milestones captured directly from real, verified work.
Each signal ties back to a specific, evidenced moment - not a self-rating.
Systems shipped end-to-end, from schema to deploy.
Gaps closed - false positives, edge cases, dead ends.
Taken from prototype to real production volume.
New tools and depth picked up on the job.
What Satyaki actually reaches for, pulled from the tags attached to captured moments - not a keyword list.
Case studies, not bullet points - impact numbers attached to each one.
Production-grade migration of 20+ AWS Glue ETL workflows from Informatica/Glue to Databricks Lakehouse for Marsh McLennan.
Scalable Databricks data validation frameworks reconciling silver and gold layers, with reusable PySpark modules for dynamic table comparison and data quality validation.
AWS Glue ETL pipelines using PySpark ingesting SAP HANA data into S3 and Snowflake for Siemens finance and billing use cases, processing 1.5+ TB daily.
Kafka-based ingestion pipelines consolidating 9+ upstream enterprise systems into a centralized Hadoop data lake for CITI.
Real-time data ingestion pipelines using Kafka, Azure Event Hub, SQL Server, and S3 for BHP mining and natural resources streaming analytics.
Data ingestion and transformation pipelines using Sqoop, NiFi, Kafka, Spark, Hive and Elasticsearch for PepsiCo vehicle telemetry analytics.
Independent builds, open-source contributions, technical writing, and experiments beyond the day job.
A combined view of your activities across the tools that you use daily (GitHub, Medium, etc. - coming soon). Add earlier projects as a graph entry if dates available.