Results-driven Data Analyst with experience designing and supporting enterprise Business Intelligence solutions using Power BI, Databricks, SQL, and Python. Skilled in developing data pipelines, dimensional data models, …
Results-driven Data Analyst with experience designing and supporting enterprise Business Intelligence solutions using Power BI, Databricks, SQL, and Python. Skilled in developing data pipelines, dimensional data models, reporting frameworks, and analytical solutions that improve data accessibility, reporting efficiency, and business decision-making. Experienced in data validation, governance, automation, stakeholder collaboration, and translating complex business requirements into scalable data solutions. Proven ability to work across cross-functional teams to deliver accurate, reliable, and actionable insights. MBA in Business Analytics with a strong foundation in statistical analysis, machine learning, and data-driven problem solving.
Conducted Customer Satisfaction (CSAT) and Voice of Customer (VoC) analytics using Python, SQL, and Genesys interaction data to identify operational drivers impacting customer experience and service performance.
Built and evaluated predictive models including Random Forest Regression and Logistic Regression while developing Databricks-based analytical pipelines to transform large-scale customer interaction data into reporting-ready datasets for Power BI analytics.
Lead development of enterprise Power BI solutions integrated with Databricks aggregated models, designing dimensional (fact & dimension) and star schema architectures to deliver scalable, analytics-ready semantic layers.
Integrate Databricks data pipelines with Power BI Service, implementing advanced DAX logic, optimized refresh strategies, and Row-Level Security (RLS) to ensure high-performance, governed reporting.
Conduct User Acceptance Testing (UAT) with cross-functional stakeholders to validate metric definitions, data accuracy, and reporting requirements prior to production deployment.
Automate data refresh workflows and reporting pipelines while documenting data lineage and transformation logic to strengthen governance, reliability, and real-time business visibility.
Conducted Customer Satisfaction (CSAT) and Voice of Customer (VoC) analytics using Python, SQL, and Genesys interaction data, Built and evaluated predictive models including Random Forest Regression and Logistic Regression, Lead development of enterprise Power BI solutions integrated with Databricks aggregated models, Integrate Databricks data pipelines with Power BI Service, Conduct User Acceptance Testing (UAT) with cross-functional stakeholders, Automate data refresh workflows and reporting pipelines
Teach R & RStudio/Posit (Predictive Modeling, Exploratory Data Analysis Statistical Analysis, Machine Learning) for 250 students.
Teach Statistical model and methods used in Business analytics (descriptive statistics, probability, hypothesis testing, linear, multiple & binary logistic regression, and regression trees).
Teach R & RStudio/Posit for 250 students, Teach Statistical model and methods used in Business analyticsGraduate Teaching Assistant | University of Wisconsin Milwaukee | Milwaukee, WIAugust 2024 – January 2026• Taught R and RStudio/Posit to approximately 250 students, covering Predictive Modeling, Exploratory Data Analysis (EDA), Statistical Analysis, and Machine Learning concepts.• Instructed students on statistical methods and business analytics techniques including descriptive statistics, probability, hypothesis testing, linear regression, multiple regression, binary logistic regression, and regression trees.• Assisted students in applying analytical and statistical concepts to real-world business problems through hands-on projects and data-driven decision making.• Supported course delivery, grading, and student mentoring while fostering analytical thinking and quantitative problem-solving skills.
Automated and modernized Power BI dashboards by migrating legacy Excel connections to Databricks-backed models, increasing data accuracy and reducing manual upkeep.
Rebuilt semantic models, advanced DAX measures, and visuals aligned to standardized business definitions; implemented Row-Level Security (RLS) and optimized dataset refresh performance.
Partnered with cross-functional teams to refine KPI definitions, document data lineage, and support version controlled deployment pipelines and automated refresh workflows in Power BI Service.
Automated and modernized Power BI dashboards by migrating legacy Excel connections to Databricks-backed models, Rebuilt semantic models, advanced DAX measures, and visuals aligned to standardized business definitions, Partnered with cross-functional teams to refine KPI definitions
Automated a complex reporting process reducing manual effort from 2 days to 2 minutes by developing Python scripts integrated with Pyramid Analytics, Power Automate, and SQL Server, significantly improving efficiency and accuracy.
Built an end-to-end Price Quotation tool using Power Apps, Power Automate, and Python, enabling users to generate accurate customer pricing within minutes instead of 3+ days, streamlining the sales process and significantly improving turnaround time.
Enhanced existing Power BI reports by integrating advanced visuals and interactive dashboards, improving data insights, usability, and enabling faster decision-making for business users.
Automated a complex reporting process reducing manual effort from 2 days to 2 minutes, Built an end-to-end Price Quotation tool using Power Apps, Power Automate, and Python, Enhanced existing Power BI reports by integrating advanced visuals and interactive dashboards
Performed market & industry research(equities) for 30 working days in Portfolio Department.
Experience on data analysis of 2 real companies using SQL and Python (pandas, NumPy, scikit-learn, and TensorFlow).
Experienced real- time trading in Trading Department.
Assisted in the Issuance of 500 Shares certificates in Shares Department.
Assisted in evaluating risk profiles and underwriting criteria using statistical models in R, improving risk assessment accuracy by 15%.
Conducted financial analysis to support pricing strategies and premium calculations, contributing to a 10% increase in profitability.
Utilized SQL to extract and analyze policyholder data, generating actionable insights to optimize customer segmentation and retention strategies.
Conducted competitor analysis and market research to support product development and pricing strategies.
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