Arun Moorthy
ETL Test Lead Test Specialist at IBM
Morrisville, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolArun Moorthy is an ETL Test Lead and Test Specialist with over eight years of hands-on experience validating complex data pipelines across banking and insurance domains. He combines deep expertise in Big Data/ETL testing, mainframe feeds, dimensional modeling (Star/Snowflake), and Slowly Changing Dimensions with practical automation using tools like QuerySurge and SSDT/SSIS. At IBM he’s led test teams for large-scale migrations and redesigns, built custom VBA validation tools, and orchestrated cloud-based batch workflows using AWS Lambda, S3, and CloudWatch. Arun has a proven track record of translating retrofit rules and VSAM-to-DB2 mappings into robust test strategies and has driven ETL automation that validated tens of millions of records. He also contributes to open-source ML infrastructure work—enhancing ONNX import and tests in the Glow compiler—demonstrating a willingness to bridge data engineering testing and machine learning systems. Based in Morrisville, NC, he pairs methodical QA discipline with a knack for identifying process improvements and knowledge transfer.
7 years of coding experience
Bachelor of Technology - BTech, Automobile Technology, First Class, Bachelor of Technology - BTech, Automobile Technology, First Class at School of Engineering and TechnologyMaster of Engineering - MEng, Product design and Commerce, First Class, Master of Engineering - MEng, Product design and Commerce, First Class at PSG College of Technology
Github Skills (8)
neural-network10
pytorch10
machine-learning10
convolutional-neural-networks10
onnx10
testing9
c-language8
cprogramming-language8
Programming languages (1)
Github contributions (5)
pytorch/glow
Apr 2019 - Oct 2019
Compiler for Neural Network hardware accelerators
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 8 commits, 19 PRs in 6 months
Contributions summary:Arun primarily contributed to the development and testing of ONNX model loading and execution within the Glow framework, a compiler for neural network hardware accelerators. Their work focused on enhancing the ONNX importer to support features such as `SAME_UPPER` and `SAME_LOWER` auto-pad modes for convolutional and average pooling operations. The user also added support for the `emotions_ferplus` ONNX model, demonstrating practical application of their contributions. Further contributions include fixing a bug related to data handling, and minor adjustments to the test infrastructure.
hardware-acceleratorscompilerneural-networkacceleratorshardware
arunm-git/glow
Apr 2019 - Jan 2020
Compiler for Neural Network hardware accelerators
Contributions:60 pushes, 18 branches, 1 comment in 8 months
hardware-acceleratorscompilerneural-networkacceleratorshardware
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