Data Analyst

Bhargavi Prodduturi Data Analyst

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

Summary

Experienced Data Analyst with 2+ years of strong background in data extraction, transformation, and analysis. Proficient in SQL, Python, R, and data visualization tools like Power BI and Tableau. Skilled in statistical analysis, data validation, and optimizing ETL processes. Adept at identifying trends, providing actionable insights, and driving data-driven decision-making. Experienced in working with large datasets, ensuring data integrity, and collaborating with cross-functional teams. Strong problem-solving skills and a passion for leveraging data to improve business outcomes and operational efficiency. Technical Skills

• Programming Languages: Python (NumPy, Pandas, Scipy), R (Linear Regression)

• Data Analysis & Tools: SQL (Window Functions, CTEs), Azure Data Factory, AWS Athena, AWS Glue, Power BI (DAX, Dynamic Slicers), Tableau (LOD Expressions), Excel

• ETL & Data Processing: Azure Data Factory, AWS Glue, Data Flow Transformations

• Statistical Analysis: Hypothesis Testing (T-tests), Correlation Analysis, Time-Series Analysis

• Data Visualization: Power BI, Tableau

• Databases & Data Sources: AWS Athena, Bloomberg API, Employee Surveys (JSON format), S&P Global Datasets

• Others: Agile Methodology, Cross-Functional Collaboration, Reporting Automation Professional Experience

Data Analyst, Mercer 10/2024 – Present Remote, USA

• Worked on Diversity, Equity, and Inclusion (DEI) Metrics Analysis, collaborating with cross-functional teams (HR, DEI Specialists) in Agile sprints, and conducting requirement-gathering sessions to define key DEI metrics, resulting in a 25% clearer reporting scope.

• Utilized advanced SQL techniques like window functions and CTEs for data extraction and aggregation from AWS Athena, optimizing ETL processes and reducing query time by 30%, with data sourced from employee surveys in JSON format.

• Led statistical analysis using hypothesis testing (t-tests) and correlation analysis to identify relationships between DEI metrics and employee engagement, providing insights into underrepresentation trends, and increasing DEI reporting accuracy by 18%.

• Leveraged Python with NumPy, Pandas, and Scipy for data cleaning, imputation, and advanced aggregation, ensuring data consistency and calculating diversity ratios, enhancing internal reports by streamlining workflows and reducing processing time by 40%.

• Performed data validation and integrity checks in the ETL pipeline using AWS Glue, ensuring 98% accuracy in employee demographic data and preventing discrepancies during the DEI data transformation and aggregation process.

• Created Power BI dashboards with dynamic slicers, drill-through capabilities, and trend analysis using DAX, allowing HR and DEI teams to visualize diversity trends, identify gaps, and make data-driven decisions, improving reporting efficiency by 22%. Associate Data Analyst, S&P Global 01/2022 – 12/2023 Hyderabad, India

• Collected data for Financial Market Trend Analysis Using S&P Global Datasets, collaborating with cross-functional Agile teams

(Product, Engineering), and led requirement-gathering sessions to align KPIs—boosting project clarity and delivery speed by 22%.

• Optimized SQL queries and ETL pipelines using Azure Data Factory to extract stock price and macroeconomic data from internal databases and Bloomberg APIs, reducing data load times by 35% and improving refresh frequency.

• Performed time-series and correlation analysis on 10 years of financial market data to identify volatility patterns and cyclical trends, improving forecasting accuracy for investment analysts by 18% through actionable visual insights.

• Leveraged Python with Pandas and NumPy to clean, transform, and analyze over 2 million rows of financial data, automating repetitive tasks and cutting data preparation time by 40% across project cycles.

• Utilized R for linear regression modeling on sector-based index returns to understand risk-adjusted performance; the model improved internal benchmark reporting reliability by 25% for investment strategy teams.

• Executed data validation and integrity checks in Azure Data Factory using Data Flow transformations, ensuring ETL pipeline accuracy above 98.5% and reducing reporting errors in published datasets used by over 120 internal users.

• Built Tableau dashboards with interactive filters and parameter controls; used Level of Detail (LOD) expressions to compare daily versus monthly trends, enhancing insights delivery and improving executive decision-making speed by 30%. Education

Sacred Heart University — Fairfield, CT, USA

Master’s Degree Computer and Information Science, Data Science Track 01/2024 – 03/2025 TKR College of Engineering and Technology — Hyderabad, India Bachelor of Engineering Computer Science 08/2019 – 04/2023

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