NAGASRI KODLI

Delivered Level 1 and Level 3 application support for McDonald's enterprise systems across 14,000+ U.S. locations in a 24/7 environment.

• Managed ServiceNow tickets across Incident, Problem, Change, and Access modules, achieving 100% SLA compliance.
• Resolved over 7,000 critical incidents, including VIP escalations, minimizing operational downtime.
• Executed advanced troubleshooting for hardware and software issues on POS systems and mobile platforms.
• Conducted root cause analyses and collaborated with Development, QA, and Infrastructure teams to implement permanent fixes.
• Developed ServiceNow dashboards and reports for real-time monitoring of incident trends and system performance.
• Monitored high-priority cases proactively, providing updates to senior leadership to prevent SLA breaches.
• Led and mentored 10–15 support engineers per shift, managing escalations and workload distribution.

1. Sales Analysis Dashboard – Power BI

· Designed and developed dynamic, interactive Power BI dashboards to analyze sales trends, revenue performance, and customer segmentation.

· Integrated multiple datasets using SQL and Power BI data modeling, ensuring accurate relationships and optimized report performance.

· Created DAX measures for KPIs such as total sales, YoY growth, average order value, and segment-wise revenue contribution.

· Conducted customer behavior and purchase pattern analysis, identifying high-value customer segments and seasonal trends.

· Delivered actionable insights that supported marketing campaign optimization and strategic sales planning.

· Built executive-level reports enabling stakeholders to monitor performance in real time.

Tools: Power BI, SQL, DAX, Data Modeling

2. Sales Performance Analysis – Python

· Performed exploratory data analysis (EDA) on retail sales data to identify top-performing products, regional trends, and revenue growth patterns.

· Cleaned, transformed, and standardized raw datasets using Pandas, improving data accuracy and reliability.

· Visualized insights using Matplotlib, highlighting sales trends, product performance, and regional comparisons.

· Conducted trend and variance analysis to uncover sales fluctuations and demand patterns.

· Generated analytical summaries that supported inventory optimization and data-driven marketing decisions.

· Improved reporting efficiency by automating repetitive analysis tasks using Python scripts.

Tools: Python, Pandas, Matplotlib

3. ETL Pipeline for E-commerce Data

· Designed and implemented an end-to-end ETL pipeline to extract, transform, and load data from multiple e-commerce sources.

· Performed data cleansing, validation, and transformation using Python and Pandas to ensure high data quality.

· Loaded structured datasets into SQLite / SQL databases for analytical reporting and querying.

· Built an automated ETL workflow using Apache Airflow, enabling scheduled daily data refreshes.

· Implemented error handling and logging mechanisms to improve pipeline reliability and monitoring.

· Ensured data consistency and integrity across datasets used for downstream analytics and dashboards.

Tools: Python, Pandas, SQL, SQLite, Apache Airflow