Sunanda Chowdary

Data Analyst with several years experience designing automated pipelines and analytics solutions for high-volume enterprises.

Delivered

Python-based ETL pipelines that increased data reliability by 35% and built Tableau dashboards that saved 12 hours of weekly reporting. Skilled in Python, SQL, Tableau, Power BI, and SAP MM/SD. Aiming to leverage data engineering and visualization expertise to accelerate decision-making and operational efficiency.

Data Analyst - Steelcase - Grand Rapids, Michigan

(2025-03)

On-site Vendor

• Architected Python ETL pipelines processing SAP MM/SD pricing data across 15,000+ product configurations, improving data reliability by 35%
• Replaced static Excel pricing trackers with SQL-driven Tableau dashboards, eliminating 12 hours of weekly manual reporting and allowing analysts to focus on higher-value analysis
• Resolved 400+ recurring data quality issues by designing and enforcing standardized validation rules, improving record accuracy from 87% to 98%
• Translated ambiguous requirements from engineering and product stakeholders into structured data models across 2,500+ enterprise configurations

Data Analyst - Tellegacy - Fargo, North Dakota

(2024-01 - 2025-02)

• Processed and standardized 250,000+ records using Python and SQL, enforcing data integrity and validation rules across a high-volume regulated environment
• Reduced reporting cycle from 4 days to 2 hours by designing and deploying 12 automated Tableau dashboards tracking operational KPIs for 40+ stakeholders
• Optimized SQL query performance by 45% through indexing strategies and join restructuring on large-scale datasets, eliminating delays on time-sensitive reporting
• Identified high-risk operational segments through exploratory data analysis, driving workflow improvements that increased efficiency by 18%

Data Analyst - Cognizant - Hyderabad, India

(2022-01 - 2023-03)

• Built Python-based demand forecasting models across 50,000+ service records achieving 88% accuracy (MAE within 12%), applied directly to resource allocation and operational planning decisions
• Designed Power BI and Tableau dashboards tracking 15 performance categories, improving efficiency metrics from 78% to 92% across operational teams
• Automated 85% of recurring operational reports using Python scripts, freeing 20+ hours of monthly analyst bandwidth previously consumed by manual processing
• Conducted root cause analysis on 3,000+ monthly records, identifying operational inefficiencies and improving overall efficiency by 25%

M.P.S - Health Informatics, Bioinformatics - Grand Valley State University (2023-01 - 2024-12)