Meet Vaghasia

Led enterprise analytics modernization by migrating legacy and manual reporting processes into a Snowflake-based cloud data warehouse. Designed dimensional models and reusable Snowflake SQL views for Power BI reporting, and implemented data validation/reconciliation and source-to-target mappings to support reliable migration and dashboard rollout.

Built a centralized multi-chain transaction platform/ledger and an AI automation platform using LLMs and RAG to reduce manual workload and improve productivity. Developed Python/SQL ETL pipelines for blockchain valuation, transaction classification, and reporting, and implemented scalable data warehouse and reporting layers across Redshift, BigQuery, PostgreSQL, and Snowflake.

Engineered scalable ETL pipelines to collect, transform, and integrate blockchain intelligence data from public data sources into analytics-ready models. Built web scraping/browsing automation components with Cloudflare handling, and optimized Google BigQuery data models for partitioning and query performance.

Analyzed and improved the backend model of Gemini AI by comparing Gemini and GPT across different scenarios. Performed data extraction and heavy data transformation/cleaning, optimized the ML model using RLHF tools, and used Python for manual corrections to Gemini responses.

Worked on SQL tasks from basic to advanced to meet client reporting requirements using Oracle, Azure Synapse, Amazon Redshift, Snowflake, and SQL Server. Designed optimized data warehouses and performed query tuning (including indexing and clustering), and automated tasks with Python.

Supported business reporting by extracting data from various platforms via web scraping and APIs and preparing report-ready outputs for management. Built and maintained ETL jobs and Tableau DataSource refresh workflows using AWS Matillion and Pentaho, and automated day-to-day processes using Python, Selenium, Katalon, Excel, and VBA.

Worked on a predictive analysis project for the US stock market, developing an algorithm to identify stock trends and predict prices for 8,000+ tickers across multiple timeframes. Integrated the algorithm into the company site (ticksignals.com) for use with other modules.

Worked on a Python Flask-based blog and Python automation scripts.

Volunteered on the Pay-N-Use project by surveying where PayNU is used in the city and helping deliver the resulting report to the Ahmedabad Municipal Corporation.

Grade: CGPA: 8.67

Completed a Bachelor of Technology in Computer Science & Engineering with a specialization in Big Data Analytics. Coursework included Big Data Analytics, Data Mining, AI, DBMS, Algorithms, and Data Structures.