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Shubham Kr. Gaud

Shubham Kr. Gaud

Data Analyst & Aspiring Data Engineer — Azure, SQL, Python, Power BI

shubham-kr
Lucknow, Uttar Pradesh, India
51 connections
Joined March 2026
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Summary

Power BI and data visualization practitioner with hands-on project experience: Shubham publishes Power BI dashboards (weather/AQI dashboard and a movies-report dashboard) on GitHub and LinkedIn that showcase end-to-end data modeling, DAX calculations and dashboard design focused on clarity and actionable insights. github+2
Aspiring data engineer with interest in building scalable pipelines and data platforms: public posts describe skills and interests in Azure, Databricks, ETL, SQL and transforming raw data into business-ready datasets to enable analytics and decision-making. linkedin
Active content sharer and learner: regularly posts technical write-ups and project summaries on LinkedIn, and holds a Data Analyst certification, indicating engagement in skills development and public sharing of work. oneroadmap
Early-career data professional with practical industry experience: listed employment as a Data Analyst at Web Craft Solution for 2023–2024 and a B.Tech in Computer Science (2019–2023), combining academic training with applied analytics work. contactout

Work

Education

Projects

Writing

Power BI Weather Dashboard – Real-Time Air Quality & Forecasting Insights

LinkedIn post describing a Power BI weather dashboard that visualizes current temperature, 7-day forecasts, AQI components, sunrise/sunset, humidity, wind speed, visibility, UV index and chance-of-rain visuals; mentions GitHub analysis and downloadable Excel file.

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Power BI Movies Report: Comedy Genre Insights

LinkedIn post summarizing a Power BI project analyzing 12K+ movies, with insights on release-year trends, genre-specific award performance, duration analysis and KPI visuals; links to GitHub resources.

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Data Engineering: The Art of Storytelling with Data

LinkedIn essay comparing data engineering to writing — outlining stages like brainstorming, drafting and editing versus data gathering, processing, analyzing and visualization, and emphasizing building pipelines that enable trustworthy, actionable insights.

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