Shrey  ·  Senior Python / Snowflake Data Engineer  ·  7+ yrs

Overview: Developed an NLP model to analyze product reviews for sentiment. Responsibilities: Utilized web scraping, word cloud, and count vectorizer to project the true sentiment of words, significantly reducing review time.

Overview: Conducted a comprehensive analysis of the ATC dataset using Big Data tools and technologies. Responsibilities: Created partitioning of desired columns in Hive, performed data transformation using PySpark, and stored data into Azure Data Lake for visualization in Power BI.

As an Associate Data Engineer, Shrey worked on 13+ Big Data modules, creating a comprehensive data pipeline for the ATC Dataset using Azure Data Factory and Synapse. He performed root cause analysis to identify improvement opportunities, enhancing efficiency by 19%. His expertise in data modeling and cloud technologies contributed to significant project outcomes.

In his personal project, ATC Dataset Analysis, Shrey utilized Big Data tools to analyze the ATC dataset. He created data partitions in Hive and transformed data using PySpark, storing it in Azure Data Lake and visualizing it with Power BI. This project showcased his ability to handle large datasets and derive insights effectively.

Shrey developed an NLP Sentiment Analysis tool that utilized web scraping and natural language processing techniques to assess product reviews. By employing word clouds and count vectorizers, he was able to save an average of 23 minutes in review time, providing quick insights into customer sentiment.