Isha Narkhede

I'm a Business Analytics & AI graduate from UT Dallas with 3+ years of experience transforming complex datasets into actionable insights. I specialize in building ETL pipelines, designing interactive dashboards, and applying predictive analytics to solve real business problems.

At Dassault Systèmes, I drove a 27% increase in product line sales through data-driven dashboards and boosted event engagement by 35% with machine learning models. At Aeronace Consultancy Services, I engineered ETL/ELT pipelines integrating data from 5+ systems and built Power BI dashboards tracking 10+ KPIs.

I'm passionate about leveraging Python, SQL, Power BI, Tableau, and emerging AI/LLM technologies to deliver measurable impact. Currently exploring how Large Language Models can take analytics to the next level.

• Engineered SQL and Python ETL/ELT pipelines integrating data from 5+ systems, improving reporting efficiency, workflow consistency, and scalability.
• Built interactive Power BI dashboards tracking 10+ KPIs, improving visibility into operational performance.
• Analyzed 50K+ records for compliance reporting, identifying anomalies, trends, and bottlenecks for business review.
• Optimized 20+ SQL queries and Snowflake data models, accelerating ad hoc analysis and reporting turnaround.
• Standardized governance and validation workflows across pipelines, improving data integrity and reducing corrections.

• Spearheaded cross-functional collaboration to gather business requirements and deliver Tableau and Power BI dashboards tracking key KPIs, driving a 27% increase in internal product line sales.
• Constructed scalable ETL/ELT pipelines to automate enterprise reporting workflows, reducing data refresh and reporting turnaround time by 40% and improving operational efficiency across 5+ recurring analytics processes.
• Implemented a machine learning–based audience engagement model in Python to analyze webinar participation behavior, achieving a 35% improvement in event engagement across internal marketing campaigns.
• Processed and analyzed 500K+ system log records using Snowflake and cloud platforms, identifying data quality issues and performance bottlenecks that improved reporting accuracy by 15%.
• Optimized data aggregation processors within Exalead CloudView in partnership with engineering teams, conducting exploratory and trend analysis to improve platform processing efficiency by 10%.

An AI-powered CRM system integrating machine learning and NLP to enhance sales, customer support, performance tracking, and internal communication. Developed a MySQL CRM analytics platform processing 10,000+ customer and sales records using SQL, and trained KNN and Feedforward Neural Network models achieving 82% accuracy for lead classification.

An end-to-end data analysis and reporting tool that empowers users to upload CSV datasets, generate clean visualizations, and create intelligent summaries using OpenAI Large Language Models. Built a Python analytics tool processing 50,000+ rows with automated cleaning, validation, and profiling. Reduced manual analysis time by 60% using EDA workflows and LLM-based insight generation. Features a user-friendly Streamlit interface with PDF and Word report export.

A data engineering and analytics solution for triaging healthcare claims denials, built using dbt for data transformation and Streamlit for interactive visualization. Leverages modern data stack practices to process and analyze claims data, enabling healthcare organizations to quickly identify, prioritize, and resolve denial patterns for improved revenue cycle management.

An interactive Excel dashboard connected to a MySQL server for real-time analysis of sales data by year, state, store, sales rep, and product category. Analyzed 25,000+ sales records using SQL joins, aggregations, and trend analysis to identify revenue patterns. Designed a dashboard tracking 6+ KPIs and cut reporting prep time by 40%. Simply refresh the Excel file to reflect the latest database updates.