Built XML metadata parser contributing revenue
DataStage → ADF/ADLS Gen/Databricks migration
Tuned PySpark on EMR (TB /
Integrated multi-source systems across AWS, Azure
Feature 01
$200K XML metadata parser
I authored a clean-OOP Python + lxml metadata parser that cut client onboarding by 25% and contributed $200K in revenue across the year.
Feature 02
DataStage → Azure migration
I led the DataStage → Azure migration on ADF + ADLS Gen2 + Databricks in a medallion architecture, dropping processing time by 20% with auditable lineage end-to-end.
Feature 03
PySpark on 15TB / 12B rows
I tuned PySpark on EMR across 15TB and 12B records, modelling star schemas on Hive and Snowflake for downstream analytics.
Feature 04
6-system multi-cloud integration
I integrated 6 source systems across AWS, Azure and GCP, clearing 30+ SIT/UAT defects and trimming an entire sprint off the release cycle.
What I shipped
Built XML metadata parser contributing $200K revenue.
DataStage → ADF/ADLS Gen2/Databricks migration.
Tuned PySpark on EMR (15TB / 12B records).
Integrated 6 multi-source systems across AWS, Azure, GCP.