Shreya Bhandari
Data Engineer II
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolShreya Bhandari is a Data Engineer II based in Seattle with nine years of experience designing and implementing data architectures across enterprise and cloud environments. She blends strong SQL Server and SSIS expertise with Python-driven machine learning workflows, and actively contributes to AWS SageMaker examples and the sagemaker-python-sdk—helping improve model deployment, network configuration, and serverless inference patterns. Her background spans business analyst and data architect roles at Amazon and Fiserv, giving her a pragmatic, product-oriented approach to data engineering. An active Kaggle participant and aspiring data scientist, she bridges data platform engineering and applied ML in production. Notably, her open-source contributions directly enhance widely used AWS ML tooling, demonstrating impact beyond her day-to-day engineering. She holds a BTech from Guru Nanak Dev University and brings a mix of hands-on implementation and community-focused improvements to ML at scale.
9 years of coding experience
4 years of employment as a software developer
BTech - Bachelor of Technology, B.tech, BTech - Bachelor of Technology, B.tech at Guru Nanak Dev University
Github Skills (18)
python10
machine-learning10
inference10
sagemaker10
amazon-sagemaker10
deep-learning10
aws10
jupyter-notebook10
testing9
documentation8
tensorflow8
pytorch8
r8
data-science7
git7
Programming languages (8)
Github contributions (5)
aws/sagemaker-python-sdk
Aug 2021 - Dec 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:304 reviews, 20 commits, 100 PRs in 1 year 4 months
Contributions summary:Shreya primarily contributed to the Amazon SageMaker Python SDK, making several fixes and enhancements. These include addressing issues in integration tests, improving network configuration for pipelines, propagating KMS keys in model deployment, and modifying image URI retrievals. They also added checks for execution roles in user settings and documentation updates regarding Fast File Mode. The user's work focused on improving the functionality and usability of the SDK for training and deploying machine learning models on Amazon SageMaker.
pytorchsagemakerdeployingmxnetpython
aws/amazon-sagemaker-examples
Oct 2021 - Aug 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:ML Engineer
ML EngineerContributions:70 reviews, 16 commits, 48 PRs in 10 months
Contributions summary:Shreya contributed to the example Jupyter notebooks demonstrating machine learning model building, training, and deployment using Amazon SageMaker. The commits involve updating and adding examples, including adding R Studio examples, and a notebook for serverless inference. The changes showcase work in deploying machine learning models and related supporting tools, and updating documentation.
pythonjupyter-notebooktrainingawssagemaker
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