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Hunter James

Data Engineer | Data Analyst | Lifelong Learner

About Me

I am a data professional with an interest in constant learning and improvement.
I have 5+ years experience working in data roles with tasks that align with those of a data analyst, database developer, software specialist, data engineer, and DBA.
I have experience with SQL, Python, R, Scala, HTML, ETL tools (SSIS and Azure Data Factory), and various business intelligence programs, applied across manufacturing, healthcare, agriculture, logistics, and government.

Work Experience

SQL Developer | General Dynamics IT (Contract-to-Hire via TEKsystems) (Nov 2022 – Present)
  • Initially contracted through TEKsystems to support the CDC; converted to a direct GDIT employee in June 2023
  • Built automated pipelines to ingest and transform flat files into Power BI dashboards, improving reporting speed by 60%
  • Engineered ETL workflows in ADF and SSIS to migrate healthcare data into Azure from SQL Server
  • Utilized Spark and Python in Databricks to process and analyze HL7 datasets with 100M+ records
  • Designed ERDs and created robust database schemas supporting migration and audit needs
  • Created and maintained stored procedures, functions, and triggers to support data migration and app updates
  • Managed full SQL Server stack (Prod, QA, Failover, Dev), ensuring uptime and performance
  • Collaborated with cross-functional teams to ensure seamless database integration for in-house applications
  • Built and documented standardized connection workflows from R, Python, and Databricks to cloud and on-prem data sources (MS SQL Server, Synapse, Azure Data Lake)
Application Specialist | Aviagen (Jan 2022 – Oct 2022)
  • Managed SQL databases and user access
  • Beta-tested program updates and designed UI wireframes
  • Provided reports using SQL, Ingres, Crystal Reports, and Qlik
  • Resolved any bugs or technical issues
  • Performed analysis to identify data quality issues
  • Ensured data met regulatory compliance
Service Intelligence Analyst | LG (Jul 2021 – Jan 2022)
  • Analyzed customer service TTS data with AWS, SAP, SQL, and Python
  • Standardized data categorization to reduce manual reporting time by 70%
  • Utilized Sprinklr to identify areas of interest and current sentiment
  • Built dynamic reports and call center analytics in Excel and BusinessObjects
  • Provided weekly reports identifying key problem products and areas
Data Analyst | Aviagen (May 2020 – Jul 2021)
  • Used statistics, Excel SQL, R, MATLAB, and Qlik Sense to organize, analyze, and present quality metrics
  • Worked with Data Manager to troubleshoot logistic issues
  • Created written and verbal reports in support of QA Management for both internal and external presentations
  • Creation and maintenance of new databases, including a multiple query database aimed at improving efficiency on a routine report by reducing man hours needed by ~90%.
  • Worked with the QA Data Manager to modernize and partially automate extra-departmental forms, as well as revise KPI thresholds and presentation of statistics on QA reports

Education

University of Illinois Urbana-Champaign (Aug 2024 - August 2026)
  • MBA
Western Governors University (Mar 2024)
  • MS Data Analytics
Royal Holloway, University of London (May 2020)
  • BS Accounting, Finance & Economics

Skills

  • Programming Languages & Tools: Python, R, SQL, Spark, Excel, Power Query, HTML
  • Databases & ETL: MS SQL Server, SSIS, Alteryx, ADF, Databricks, Redshift, Snowflake, Synapse, Ingres
  • Cloud & Platforms: Azure, AWS (S3, Redshift), Foundry
  • Visualization & Reporting: Power BI, Tableau, Qlik Sense, SAP BusinessObjects
  • Languages: Spanish (B2 Proficiency)

Projects

Portfolio Flask App – A Python Flask-based web application deployed on AWS EC2 with Nginx, Gunicorn, PostgreSQL (RDS) and GitHub CI/CD. Built to demonstrate my skills in data engineering deployment, automation, and cloud infrastructure.
Review Categorization – For my capstone project, in Python, I utilized RNNs to predict the star rating customers left products based on the content of the reviews. I then created an application that processes sets of reviews using the trained RNN and assigns them a rating.
Customer Churn – An extended exploratory and predictive analysis of customer churn for a telecommunications company.
Customer Churn - Logistic Regression – A predictive analysis of customer churn utilizing logistic regression with limitations noted and recommendations given.
Customer Segmentation - K Means – An analysis of customer segmentation using K Means clustering to identify distinct customer groups based on income and monthly charge.
Feature Selection- Principal Component Analysis – An analysis of feature selection using Principal Component Analysis (PCA) to reduce dimensionality and improve model performance.
Market Basket Analysis – An analysis of market basket data using association rule mining to identify product associations and improve sales strategies.
Predicting Customer Bandwidth Usage – An analysis of customer bandwidth usage using regression analysis to predict future usage patterns and optimize network resources.
Car Comparison and Dashboard – An exploratory data analysis (EDA) comparing car features and performance metrics with the data presented in Tableau.

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