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Shivya GargSG
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Shivya Garg

@shivyagarg

Data analyst and data engineer specializing in Azure-powered supply chain analytics.

India
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What I'm looking for

I seek roles building scalable data pipelines, analytics platforms, and GenAI solutions in collaborative teams focused on operational impact and continuous improvement.

I am a data analyst and engineer who builds scalable Azure Databricks and Azure Data Factory pipelines, enterprise dashboards, and GenAI solutions to improve supply chain and transportation visibility. At Philips, I consolidated reporting, automated ETL, and delivered operational insights across global logistics programs, reducing reporting complexity and infrastructure costs.

I prototype conversational and RAG-based AI tooling to empower business users, and have implemented automated finance workflows, Power BI and QlikSense dashboards, and GenAI assistants using LangChain, ChromaDB, and Microsoft Copilot Studio. I focus on practical, maintainable systems that drive measurable operational improvements.

Experience

Work history, roles, and key accomplishments

Philips logoPH
Current

Specialist - Business Analysis

Aug 2024 - Present (1 year 7 months)

Improved global transportation analytics visibility across 35+ logistics projects by designing scalable SQL data models and automating end-to-end pipelines in Azure Databricks and Azure Data Factory, consolidating 35+ reports into 18 dashboards and cutting reporting infrastructure costs by 50%.

Philips logoPH

Data Engineer Intern

Jan 2024 - Jul 2024 (6 months)

Automated finance and internal pay-control workflows using Azure Databricks and Python, saving ~4–5 operational hours per cycle and built Power BI and QlikSense dashboards with automated alerts to improve financial KPI visibility.

Education

Degrees, certifications, and relevant coursework

SRM University logoSU

SRM University

Bachelor of Technology, Artificial Intelligence

2020 - 2024

Grade: CGPA: 8.9

Completed a Bachelor of Technology in Artificial Intelligence with a CGPA of 8.9, focusing on AI, data engineering, and applied machine learning.

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