Bright Rikin

Designed and deployed a streaming batch geospatial ETL pipeline using Apache NiFi to automate ingestion, transformation, and loading of GeoJSON power line data into PostgreSQL/PostGIS. Engineered data validation and regex-based transformation workflows to handle malformed and semi-structured inputs, improving data quality and reliability to ensure reliable storage of LineString geometries for scalable spatial analysis.

The E-Commerce Sales Performance Dashboard is an interactive Tableau dashboard built to analyze year-to-date sales, profit, market share, product category performance, and customer acquisition trends for a global e-commerce business. The dashboard helps business stakeholders quickly monitor revenue performance, identify underperforming categories, compare regional market contributions, and understand customer acquisition movement across key markets. Using Excel, SQL, and Tableau, I cleaned, structured, analyzed, and visualized e-commerce sales data to support faster and more informed business decisions.

Built a scalable, serverless AWS data pipeline to capture, process, and analyze real-time IoT energy data. Leveraged AWS IoT Core, Lambda, Kinesis Firehose, S3, Glue, Athena, and Terraform to create a secure, automated, and analytics-ready data platform.

HR Attrition & Workforce Insights Dashboard is a Tableau dashboard designed to help HR teams and business leaders understand employee turnover patterns across departments, job roles, age groups, gender, education fields, and job satisfaction levels. The dashboard tracks key workforce KPIs including total employees, attrition count, attrition rate, active employees, and average age, while providing visual insights into where attrition is most concentrated. The project solves a common HR problem: identifying which employee segments are at higher risk of leaving and where retention efforts should be prioritized. By turning raw HR data into an interactive, decision-ready dashboard, this project supports better workforce planning, employee engagement analysis, and data-driven retention strategies.

SolPump Analyzer is a Python-based crypto analytics and automation project designed to identify high-momentum Solana memecoins by combining real-time market data, social sentiment signals, risk checks, and automated trade logic. Built in Jupyter Notebook, the project uses APIs, web scraping, blockchain libraries, and rule-based scoring to rank tokens based on liquidity, volume, social hype, and safety indicators. The project solves a common problem in high-volatility crypto markets: traders often rely on scattered information across token charts, social media, and risk-checking platforms. SolPump Analyzer brings these signals into one automated workflow, helping identify trending tokens faster while applying structured risk filters before trade execution.