Power Bi Azure Data Analyst

Manasa Swetha Tiramareddy

813-***-**** ***************@*****.*** LinkedIn Github

EDUCATION

University of South Florida, Muma College of Business, Florida Aug2022-May 2024 Master of Science in Business Analytics, and Information Systems GPA: 3.93 Amrita School of Engineering, Kerala Jun 2017-Jun 2021 Bachelor of Technology: Electronics and Communication Engineering GPA: 3.87 TECHNICAL SKILLS

Data Analysis: SQL, Python, R, Excel (VLOOKUP, PivotTables, INDEX MATCH, IF, SUMIF), VBA Data Visualization: Power BI including DAX, Tableau, Qlik Sense, Google Data Studio Database Management: Oracle, Azure SQL, PostgreSQL, MySQL ETL Processes: SSIS, PySpark, AWS DMS, Azure Data Factory, Hadoop Big Data Technologies: Hadoop, Hive, Apache Spark, AWS S3, Azure Data Lake Machine Learning & AI: Scikit-learn, TensorFlow, Regression and Classification models, NLP Programming Languages: C++, Delphi, JavaScript

Data Integration: Data Migration, Data Quality Assurance, Data Validation System Administration: System Health Checks, Disaster Recovery, JIRA (Service Level Agreements) Microsoft Office Suite: MS Word, MS Excel, MS PowerPoint Miscellaneous: C, C++, Java.

EXPERIENCE

EMI Industries through Robert Half

Report Developer Sep 2024-present

• Developed a Power BI Sales Performance Dashboard to analyze sales growth, customer acquisition, and product profitability, driving better business decision-making for a manufacturing firm.

• Integrated and cleaned data from CRM and ERP systems (Macola and SAP) using MySQL Studio, SQL queries, and Prophix, ensuring accurate and up-to-date information for robust sales and financial analysis.

• Designed advanced KPIs and dynamic dashboard reports using DAX formulas to measure monthly sales growth, customer retention, and product profitability.

• Developed comprehensive financial dashboards covering P&L, Balance Sheet, Revenue, COGS, Net Income, and Customer Profitability, with a focus on trailing twelve months (TTM) for revenue and COGS analysis.

• Created drill-through capabilities for financial dashboards in Power BI allowing deep analysis of financial and operational data, enabling stakeholders to take informed actions quickly.

• Leveraged Prophix, MySQL Studio, SQL queries, Excel and Power BI to perform in-depth financial analytics in a manufacturing firm, driving operational efficiency, profitability insights, and improved decision- making across teams.

• Reported directly to the CFO, delivering insights on revenue trends, profitability analysis, and cost optimization, which improved strategic decision-making and contributed to a 15% increase in operational efficiency. Gradate Research Assistant, University of South Florida Aug 2023-May 2024

• Implemented ETL pipelines to extract, transform, and load customer support metrics from multiple sources.

• Created efficient SQL queries to analyze large datasets, optimizing query performance and reducing response time.

• Collaborated with stakeholders to define requirements and provide actionable insights through data analysis.

• Developed real-time performance dashboards in Power BI and Tableau, monitoring agent performance and improving service levels.

• Ensured seamless data integration and standardization, improving data consistency across the organization. Dish Network Technologies

Senior Data Analyst Oct 2021-Jun 2022

• Developed and deployed a financial forecasting model using Python and SQL, improving forecast accuracy by 20%, enabling more precise budgeting and financial planning.

• Created a Power BI dashboard integrating data from multiple financial systems, providing executives with real-time insights into revenue, cost trends, and profitability, accelerating decision-making.

• Designed DAX-powered KPIs to track critical metrics such as gross margins, operating costs, and net income, delivering enhanced clarity on financial performance.

• Conducted a comprehensive cost optimization analysis, identifying inefficiencies in vendor management and internal operations, resulting in a 15% reduction in overhead costs.

• Automated financial reporting workflows, streamlining data integration from ERP systems and significantly reducing the manual effort required for monthly reporting.

• Delivered strategic recommendations to the executive team based on deep financial analysis, contributing to a 10% improvement in resource allocation and operational efficiency. Tata Consultancy Services

Data Analyst Jan 2021-Oct 2021

• Streamlined ETL processes using Oracle SQL, reducing data integration processing time by 30%, which improved overall data flow efficiency for business analysis.

• Managed a financial data warehouse in Oracle 19c, implementing data standardization and cataloging techniques to ensure accurate and consistent financial data for reporting and analysis.

• Developed and optimized complex SQL queries for efficient financial data retrieval and reporting, enhancing real-time business insights.

• Built real-time financial dashboards in Power BI and Excel (Pivot tables), providing stakeholders with enhanced visibility into key financial KPIs, budget performance, and other business-critical metrics.

• Applied data mining techniques to uncover patterns and trends in financial data, significantly enhancing business intelligence and enabling more informed decision-making.

• Collaborated with finance and business teams to translate data insights into actionable strategies, driving improvements in financial planning and resource allocation.

PROJECTS

Data Extraction and Analysis - Infobae

• Led data analytics initiative to integrate Marfeel API for content optimization at Infobae, enhancing user engagement by generating relevant news articles based on reader behavior.

• Developed data-driven recommendation systems using Marfeel data to suggest similar high-viewership articles, resulting in a 20% increase in content click-through rates.

• Utilized Python and SQL for data extraction, transformation, and loading (ETL) processes, ensuring seamless integration of Marfeel data into Infobae's analytics pipeline.

• Analyzed user interaction patterns to translate and publish trending content across multiple languages, increasing global readership by 15%.

• Automated reporting and dashboards in Tableau to monitor content performance metrics, providing actionable insights that guided editorial strategies and improved reader retention. Customer Analytics for E-commerce

• Developed a customer analytics platform using Tableau, Qlik sense and Excel providing insights into buying patterns and behaviors.

• Analyzed data from multiple sources, including online transactions and customer feedback.

• Created interactive visualizations to highlight key trends and opportunities for personalized marketing.

• Collaborated with marketing teams to use data insights for targeted campaigns, increasing customer engagement.

• Contributed to a 20% increase in customer retention by identifying key factors influencing customer satisfaction. Troubleshooting Power BI Dashboards

• Data Integration: Aggregated and cleaned credit card transaction and customer demographic data using SQL, ensuring a unified dataset for analysis.

• Data Modeling: Built a data model with fact and dimension tables in Power BI, enabling detailed analysis of credit card usage and customer behavior.

• DAX Calculations: Developed advanced DAX measures to calculate customer lifetime value (CLV), churn risk, and average transaction value.

• Interactive Dashboard: Designed visualizations for customer segmentation, spending patterns, and geographic analysis, highlighting key trends and insights.

• Actionable Insights: Identified high-value customer segments and churn risks, leading to targeted marketing strategies and improved customer retention efforts. Predictive Analytics for Online Shoppers Intentions

• Spearheaded a comprehensive big data project to address declining customer engagement, rising bounce rates, and diminishing revenue in the e-commerce business.

• Applied Logistic Regression, Decision Trees, and Random Forests to forecast customer intent, informing website design and marketing strategies in the B2C online shopping segment.

• The analysis suggests that while a Random Forest model excels in predictive analytics within e- commerce. platform with high F1 scores and AUC values.

• The linear SVC model outperforms overall, especially in accuracy, F1 score, and balanced precision- recall making Linear SVC ideal for handling imbalanced datasets.

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