Data Analyst Power Bi

Halika Sajja

*************@*****.*** +1-203-***-**** www.linkedin.com/in/halikasajja Delray Beach, FL Professional Summary

An analytical and results-driven Data Analyst with experience in health-tech and fintech. Proven expertise in leveraging the Azure ecosystem (Data Factory, Synapse, Databricks) to build end-to-end ETL/ELT pipelines, develop predictive machine learning models, and create insightful Power BI dashboards. Adept at using Python and SQL to translate complex data into actionable business strategies and drive operational efficiency.

Technical Skills

Cloud & Big Data: Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Blob Storage, Azure SQL, Azure ML, Apache Spark, Delta Lake BI & Visualization: Power BI, DAX, Power Query, Tableau, Matplotlib, Seaborn Programming & Databases: Python (Pandas, NumPy, Scikit-learn), SQL (T-SQL), NoSQL ETL & Data Modeling: SSIS, Dimensional Modeling, Star Schema, Snowflake Schema Machine Learning: Regression, Classification, Forecasting (Scikit-learn, XGBoost), NLP Tools & Methodologies: Git, GitHub, Agile/Scrum, JIRA Education

Florida Atlantic University – M.S. in Data Science and Analytics Boca Raton, FL Graduated: April 2025

VNR Vignana Jyothi Institute of Engineering and Technology – B.Tech in Information Technology Hyderabad, India

Graduated: May 2023

Work Experience

Optum Miami Data Analyst May 2024 – Present

● Engineered scalable ETL pipelines using Azure Data Factory and Azure Databricks to process patient data into Azure Synapse Analytics, supporting analysis for a user base of 50+ clinicians.

● Developed dynamic Power BI dashboards with complex DAX measures to monitor patient outcomes, improving strategic planning by 20%.

● Built and deployed a patient readmission classification model using Azure ML, achieving 88% accuracy and enabling proactive care interventions.

● Authored optimized T-SQL queries and stored procedures to perform complex transformations and ad-hoc analysis on Azure SQL Database. UST Hyderabad Data Analyst June 2022 – May 2023

● Analyzed 1M+ transactions using SQL and Python to identify fraudulent patterns, contributing to a 15% improvement in fraud detection model accuracy.

● Automated daily operational reports using Power BI, reducing manual effort by over 15 hours weekly and providing real-time insights to leadership.

● Performed data cleaning and feature engineering on semi-structured financial data in Azure Blob Storage for a customer segmentation initiative.

● Collaborated in an Agile/Scrum environment using JIRA and Git for sprint planning, version control, and team collaboration. Projects

Hospital Readmission Prediction System

● Developed a classification model in Python (Scikit-learn) to predict 30-day patient readmission, leading to more efficient clinical resource allocation.

● Deployed the model as a real-time predictive service using Azure ML Studio and visualized patient risk scores on an interactive Power BI dashboard.

● Utilized T-SQL to extract and preprocess training data from Azure SQL, ensuring high-quality inputs for the model.

E-commerce Demand Forecasting Engine

● Constructed an end-to-end ETL pipeline with Azure Data Factory and Azure Databricks to populate a central data warehouse in Azure Synapse Analytics.

● Implemented a time-series model in Python to forecast product demand, reducing overstocking costs by a projected 18% and improving inventory turnover.

● Created Power BI reports to track forecast accuracy and visualize sales trends, providing actionable insights to the supply chain team. Real-Time Financial Market Sentiment Analysis

● Engineered a serverless pipeline using Azure Functions for real-time news ingestion, performing NLP-based sentiment analysis with Python (NLTK).

● Stored processed sentiment scores and article metadata in Azure Blob Storage for historical analysis and model retraining.

● Designed a live-streaming Power BI dashboard to correlate market sentiment with stock prices, providing traders with immediate, data-driven insights.

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