Shraddha Kurra

Data Engineer

Based in Detroit, MI, USA

About

With 4+ years of work experience. My proficiency includes programming languages such as Python, R, and SQL and data engineering tools like Apache Airflow, Hadoop, and Spark. I specialize in data pipeline orchestration, ETL development and optimization, Data Migration and data visualization using Tableau, Power BI, Excel, and IBM Cognos Analytics. I possess strong analytical and problem-solving skills with a focus on long-term vision. A highly motivated, easy-going team player with a cheerful disposition, I excel at multitasking and thrive in collaborative environments.

Skills

Projects

Texas Hospital Reimbursement rates analysis

This project involves analyzing outpatient care reimbursement rates in hospitals across Texas, in collaboration with the healthcare club at UTD.

Emergency room visits

Data Visualization of Emergency room visits. Visual analysis of Patient wait time and satisfaction using Tableau.

Flu shots 2022

Financial Analysis Dashboard

This data visualization project has the financial analysis of sales data. It gives insights like a) Total Revenue b) Total sales representatives c) Total revenue by region and gender d) Total revenue of products by rank e) Total revenue generated is compared to average revenue etc

IBM Data Analyst Capstone Data Visualization

The visualization provides insights into the survey data on Current technology usage, Future Technology trends and demographics.

Experience

January 2024-Feb 2025

Data Engineer at Kian Corporation

May 2020 - August 2022

Data Engineer at Stride Organics Pvt Ltd

June 2019 - April 2020

Data Analyst at Mylan

March 2025 - Present

ETL Developer/ Data Engineer at Humana (contract)

• Develop and optimize SQL Server ETL pipelines supporting Medicaid/Medicare state submissions, validating and processing 20+ monthly reports with 1M+ records per report. • Translate BRD/TRD requirements into automated T-SQL validation rules and exception outputs, ensuring submission-ready, audit-compliant datasets. • Build reusable dynamic SQL frameworks and stored procedures to standardize ingestion, transformation, validation, and loading across multiple pipelines, reducing rework and speeding delivery. • Reduced end-to-end runtime by 50% (40 to 20 minutes) through orchestration and reusable ETL frameworks, improving overall delivery efficiency.

Education

Aug 2022- May 2024

Masters in Business Analytics at UT Dallas

Masters of Science in Business Analytics and a graduate certificate in Health Information Technology