Tejarani Banna

About Tejarani Banna

Driven Data Solutions Engineer with 4+ years of experience architecting and delivering scalable data pipelines, advanced analytics, and predictive models that empower strategic decision-making. Expert in designing robust ETL workflows, optimizing database performance, and crafting dynamic dashboards using Power BI, Tableau, and Python to transform complex data into actionable insights. With a Master of Science in Information Technology from the University of Cincinnati, I combine deep expertise in statistical analysis, machine learning, and data visualization to solve high-impact problems across academia, healthcare, and technology sectors. Proven ability to lead cross-functional initiatives that improve data accuracy, reduce latency, and enhance operational efficiency. ConCurrently advancing data-driven solutions at Community Dreams Foundation, while also recognized as an researcher for work in predictive analytics and healthcare. Certified in Python, SQL, and Ethical Hacking, I am passionate about leveraging data innovation to accelerate growth and foster continuous improvement. Connect with me to explore data engineering, machine learning, and scalable data architecture strategies. Proven track record of delivering impactful projects, including: Blinkit Analysis: Extracted and cleaned Kaggle data to build dynamic Power BI dashboards showcasing sales trends, customer behavior, and product performance—enabling real-time business insights and improved operational efficiency. Business Analysis: Developed interactive Tableau dashboards and detailed reports through Python and SQL data preprocessing and EDA, integrating ML techniques to uncover patterns and support data-driven decision-making. Heart Disease Prediction System – Developed a machine learning model using Python to predict heart disease with high accuracy, leveraging data cleaning, feature engineering, and model evaluation techniques. Crop Yield Prediction System – Designed a predictive model using Linear Regression to forecast crop yields, optimizing the prediction process by comparing various algorithms. Early Diabetes Prediction – Built a machine learning model to predict diabetes with 86% accuracy, utilizing Python and scikit-learn for data preprocessing and classification. Music Recommendation System – Created a deep learning-based recommendation engine using TensorFlow to suggest music based on facial expressions, combining computer vision and machine learning for personalized recommendations.

Experience

1. Data Engineer

   Community Dreams Foundation

   Jun 2025 — Present · US

   Designed and deployed AWS Glue & Python-based ETL pipelines to automate data ingestion from multiple sources into AWS S3 Data Lake, reducing processing time by 30%.Modeled data in Amazon Redshift Data Warehouse for analytics dashboards, reducing slow-running queries from several minutes to under 30 secondsIntegrated AWS Step Functions to coordinate multi-step ETL workflows, improving reliability and reducing reprocessing errorsEstablished data quality checks and governance protocols, achieving 99% data accuracy in production dashboards.Collaborated with data scientists and analysts to translate business needs into technical solutions, enabling 30% faster decision-making.Performed large-scale data integration from multiple structured and unstructured sources into AWS S3 and Redshift.