Shiv Sood
Senior Software Engineer at Microsoft
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolShiv Sood is a Senior Software Engineer with 7+ years at Microsoft and over two decades in telecom and device platforms, currently shaping hyper-scale, enterprise-grade big data products on the Azure Data team. He blends hands-on engineering, product management, and architecture to deliver end-to-end analytics and ML solutions, often partnering directly with customers and partners to drive adoption. His background ranges from low-level protocol stacks and mobile SDKs at Nokia and Symbian to service execution and charging systems at Huawei, giving him rare depth across systems, networking, and cloud data services. On GitHub he has contributed PySpark-based ML work to Microsoft’s widely used sql-server-samples, demonstrating practical model development and export workflows for production scenarios. Known for moving fluidly between deep technical implementation and product strategy, he focuses on building scalable, observable systems that translate data science into reliable enterprise features.
7 years of coding experience
19 years of employment as a software developer
2000 Computer Sciences, 2000 Computer Sciences at National Institute of Technology WarangalGovernment College, Sanjauli
English
Github Skills (10)
logistic-regression10
machine-learning10
pyspark10
data-analysis9
jupyter-notebook9
eval8
evaluation8
data-preprocessing7
hdfs7
dataprep7
Programming languages (4)
Github contributions (5)
microsoft/sql-server-samples
Apr 2019 - Aug 2020
Azure Data SQL Samples - Official Microsoft GitHub Repository containing code samples for SQL Server, Azure SQL, Azure Synapse, and Azure SQL Edge
Role in this project:
Data Scientist
Data ScientistContributions:25 commits, 18 PRs, 6 pushes in 1 year 5 months
Contributions summary:Shiv's commits focus on machine learning model development within a PySpark environment. They are implementing and modifying a Jupyter notebook that explores data, prepares it for training, trains a logistic regression model, and evaluates its performance. Their work includes data exploration, feature selection (age and hours_per_week), and model evaluation using metrics like AUC. Furthermore, the user persists models, including saving it in a format ready for exporting.
azure-synapsesqlserversql-serversqledge
shivsood/sql-spark-connector
Jul 2020 - Sep 2023
Apache Spark Connector for SQL Server and Azure SQL
Contributions:10 pushes, 11 branches in 3 years 2 months
sql-serverconnectorsqlspark-connectorapache
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