Priyanka Pachauri

Results-driven Data Analyst with hands-on experience in analyzing large datasets, developing interactive dashboards, and delivering actionable business insights. Proven expertise in SQL, Excel, Python, and Power BI for data analysis, validation, and visualization. Skilled in tracking KPIs, campaign performance metrics, and ROI optimization.

Strong analytical mindset with ability to translate complex data into clear recommendations for stakeholders. Eager to apply data-driven problem-solving skills to support marketing analytics and business performance optimization.

Data Analytics Intern at Ernst & Young (EY) (2025-07 – 2025-09)

Sales Performance & Customer Insights Analytics Project

• Analyzed large datasets containing sales transactions, customer demographics, and product performance to derive actionable business insights for stakeholder decision-making
• Developed interactive Power BI dashboards to track KPIs including Total Sales, Profit Margins, Customer Acquisition, and Regional Performance across multiple business segments
• Performed data validation and quality checks using Power Query and Python to ensure accuracy of reporting outputs before dashboard deployment
• Identified trends and patterns in customer engagement metrics, leading to recommendations that improved ROI understanding by 25%
• Collaborated with cross-functional teams (marketing, sales, technology) to optimize campaign outcomes through data-driven insights and visualization
• Translated complex data into clear, actionable recommendations for business stakeholders through comprehensive reports and dashboard presentations
• Implemented dynamic filters and slicers for time-period, region, and product category analysis, enabling stakeholder self-service analytics
• Performed comprehensive data validation and quality checks across multiple datasets containing customer, channel, and campaign metadata to ensure data governance compliance
• Developed Python automation scripts using Pandas and NumPy to streamline data reconciliation processes, reducing manual validation time by 60%
• Identified data quality issues and generated validation reports highlighting invalid mappings, missing values, and data inconsistencies for stakeholder review
• Ensured adherence to data accuracy standards and compliance requirements throughout the data processing pipeline
• Delivered analysis-ready datasets in standardized formats for reporting and analytics platform integration

Bachelor of Technology (B.Tech) in Computer Science Engineering – Dr. A.P.J. Abdul Kalam Technical University (AKTU) (2026)