Data Analyst with SQL, Python, and BI Expertise

Naga Nainika Bapathu Data Analyst

***********@***********.*** 201-***-**** USA LinkedIn Portfolio

Summary

Detail-oriented Data Analyst with 2+ years of experience in data extraction, transformation, and analysis to support data-driven decision- making. Proficient in SQL, Python, and R Advanced Excel for statistical analysis and data manipulation. Skilled in designing and automating ETL workflows using cloud platforms like AWS and Azure. Experienced in creating interactive dashboards with Power BI and Tableau to visualize key insights. Strong collaborator with cross-functional teams, adept at ensuring data quality, validation, and delivering actionable business insights.

Technical Skills

Languages: SQL (Advanced SQL, Window Functions, CTEs), Python (NumPy, Pandas, SciPy, Matplotlib, Seaborn), R, SAS, PySpark

Excel: Advanced Excel (Pivot Tables, Power Query, Power Pivot, VLOOKUP/XLOOKUP, INDEX-MATCH, Advanced Formulas, Conditional Formatting, Macros, Data Modeling)

ERP & SAP Technologies: SAP S/4HANA, SAP ECC, SAP BW/4HANA, SAP HANA SQL, SAP ERP Data Extraction, Integration with SAP Sales, Finance, and Supply Chain modules

Cloud & ETL Tools: AWS Glue, AWS Athena, AWS Lambda, AWS Glue DataBrew, Azure Data Factory, Azure Data Lake, Google BigQuery, Apache Airflow

Databases: AWS S3, Azure SQL Database, ERP, POS Systems, MySQL, PostgreSQL, Oracle, Snowflake

Data Visualization: Power BI (DAX, drill-through, slicers, custom KPI cards), Tableau (dynamic filters, drill-down, LOD calculations), Matplotlib, Seaborn, Plotly

Data Analysis: Exploratory Data Analysis, Cohort Analysis, RFM Analysis, Statistical Hypothesis Testing, Regression Analysis, Predictive Modeling, Time Series Analysis, Forecasting

Methodologies: Agile Collaboration, Requirement Gathering, Data Validation and Quality Checks, Automated Reporting, Version Control (Git), Documentation

Professional Experience

Data Analyst, American Family Insurance 10/2024 – Present Remote, USA

Worked closely with Marketing, Sales, and IT teams using Agile methodology and led requirement gathering sessions to segment customers by demographics, policy types, claims history, and behaviors to optimize targeted marketing campaigns.

Utilized SQL and advanced SQL techniques such as window functions and common table expressions to extract and join data from policy databases and CRM systems stored in AWS S3 buckets while performing ETL with AWS Glue and Athena.

Conducted exploratory data analysis and statistical hypothesis testing to uncover customer trends and validate segment distinctions, improving segmentation accuracy by 18% to support more effective marketing strategies.

Employed Python libraries including NumPy, Pandas, and SciPy to preprocess data and perform cohort and RFM (Recency, Frequency, Monetary) analysis to evaluate customer lifetime value and propensity to purchase new insurance products.

Leveraged advanced Excel (Pivot Tables, Power Query, and complex formulas) to perform ad hoc analysis, validate datasets, and support business users with quick-turnaround reports alongside system-driven BI solutions.

Ensured data validation and quality during ETL workflows by implementing programmatic data integrity checks using AWS Glue DataBrew and Lambda functions, reducing data errors by 22% and maintaining consistent data pipelines.

Developed interactive Power BI dashboards featuring drill-through, slicers, and custom KPI cards with DAX formulas to provide real- time insights on customer segments and campaign performance, helping increase marketing ROI by 15%.

Data Analyst, PepsiCo 01/2022 – 08/2023 Hyderabad, India

Collected and integrated sales data from retail, e-commerce, and distributors, collaborating with sales, marketing, and IT teams to analyze regional performance and identify revenue opportunities.

Developed optimized SQL queries on Azure SQL Database and built ETL pipelines using Azure Data Factory to ingest ERP and POS data, improving efficiency by 25%, and performed rigorous data validation to ensure consistent multi-source reporting.

Conducted cohort and time series analysis to uncover seasonal sales patterns and SKU lifecycle trends, supporting marketing strategies and inventory planning, and applied R regression analysis to measure promotional campaign impact, increasing promotion ROI by 15%.

Used Python with Pandas and NumPy for data cleaning, manipulation, and exploratory analysis, automating monthly reports and reducing manual effort by 40%.

Created interactive Tableau dashboards with filters, drill-downs, and LOD calculations to visualize insights for leadership, enabling data-driven decisions across regions and customer segments.

Education

New Jersey Institute of Technology – Newark, NJ

Master of Science in Information Science/Studies 08/2023 – 05/2025

Gokaraju Rangaraju Institute of Engineering and Technology – Hyderabad, India

Bachelor of Technology in Computer Science 06/2019 – 05/2023

Certifications

Certified Entry-Level Python Programmer from OpenEDG

Oracle Cloud Infrastructure 2025 Certified Generative AI Professional

NPTEL Data Science for Engineers.

Introduction to MS Excel

Power BI for beginners

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