Kevin Braman

About Kevin Braman

I am a data engineering and automation professional with a unique dual background in Computer Science and Accounting, bridging technical expertise with business insight. My work centers on building scalable ETL pipelines, automating workflows, and streamlining data processes to improve operational efficiency and reduce manual effort. At my current role, I’ve designed and maintained Python-based ETL pipelines that process 100K+ rows of SQL Server data, reducing a 30-step manual reporting process to just 2 steps. I also leverage Power BI, Excel, and SQL to deliver actionable insights that support cross-functional teams and drive business outcomes. I am currently pursuing my Master\'s in Data Science at the University of Texas. My core technical skills include: Languages: Python, Java, R, SQL (T-SQL) Frameworks & Libraries: Flask, pandas, NumPy, Matplotlib, sqlalchemy Tools & Platforms: Git, Docker, Google Cloud Platform, VS Code Software: Microsoft Excel, Microsoft SQL Server, Power BI, Oracle NetSuite I’m passionate about using automation and data-driven solutions to solve complex business problems and am seeking opportunities in data engineering, ETL development, and process automation. Checkout my projects on GitHub: github.com/kevinbraman92

Experience

1. Regional Operations Analyst

   Datavant

   Nov 2022 — Present · US

   Analyzed data across multiple healthcare regions to improve medical record retrieval efficiency using the ChartFinder system, and escalated issues with under-performing provider sites.• Supported regulatory reporting initiatives by preparing and analyzing healthcare data for HEDIS, CMS, and RADV audits, ensuring compliance and improving data accuracy for medical record retrieval.• Utilized Power BI to explore and analyze healthcare region data, generating insights that supported process improvements and increased medical record retrieval yields.• Built and maintained automated ETL pipelines in Python to extract and process 100K+ rows of operational data from Microsoft SQL Server, improving data accessibility and reducing turnaround time for key reports.• Integrated SQL querying and Python scripting to streamline the retrieval and processing of large datasets for recurring business processes and stakeholder reporting.• Built and maintained an automated ETL pipeline in Python using pandas and RapidFuzz to extract andstandardize 100K+ rows of operational data from Microsoft SQL Server, reducing a 30-step manual reporting process to just 2 steps. Streamlined recurring workflows by automating data retrieval, transformation, and export, significantly reducing error rates and manual effort.• Extracted EMR provider data from Microsoft SQL Server using the Python pandas library, applied formatting and validation logic to filter invalid or out-of-region phone numbers, reducing human error and completely streamlining a previously manual process

Education

• Lone Star College

  Associate’s Degree, Science

• Oregon State University

  Bachelor of Science - BS, Computer Science

• The University of Texas at Austin

  Master of Science - MS, Data Science

• Sam Houston State University

  Bachelor’s Degree, Accounting