Data Engineering Intern- Summer 2027

Posted 5 Hours Ago
Be an Early Applicant
North Richland Hills, TX, USA
In-Office
28-28 Hourly
Internship
Transportation • Industrial
The Role
Develop software and analyze data to improve data-team efficiency and construction operations. Build, test, and support ETL/ELT pipelines; contribute to Power BI dashboards; automate workflows with Azure services; integrate operational and telematics systems; and participate in Git-based development, testing, documentation, code reviews, and CI/CD. Troubleshoot data quality, refresh, and performance issues while collaborating with experienced data engineers.
Summary Generated by Built In
Data Engineering Intern – Summer 2027
Location: North Richland Hills, TX
Pay: $27.50/hour
Schedule: 40 hours/week | 10-Week Summer Internship
Housing: Provided for eligible interns
About Ragle
Since 1993, Ragle Inc. has built infrastructure across Texas and beyond, specializing in highway, bridge, excavation, grading, and other heavy civil construction.
We're looking for a Data Engineering Intern to join our team for a 10-week summer internship. You'll develop software, analyze data, and help automate workflows that improve efficiency for the data team and support real construction operations while learning from experienced professionals.
What You'll Do
  • Develop software and analyze data to improve efficiency for the data team
  • Use Python, Node.js, SQL, and Azure Container Apps Jobs to automate workflows and streamline data processes
  • Help build, test, and support ETL/ELT pipelines for operational, fleet, toll, telematics, and compliance data
  • Contribute to Power BI dashboards and datasets that track equipment activity, geofence events, and business performance
  • Support workflow automation using tools such as Azure Functions, Power Automate, and Azure Event Grid
  • Assist with data integrations involving systems such as Samsara, Gauge, and NTTA
  • Participate in Git-based development, documentation, testing, code reviews, and CI/CD practices
  • Troubleshoot data quality, refresh, and performance issues with guidance from the data engineering team
What We're Looking For
  • Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, Software Engineering, Analytics, Engineering, or a related field
  • Basic experience with Python, JavaScript or Node.js, SQL, databases, APIs, Power BI, or cloud technologies through coursework, projects, or previous experience
  • Basic understanding of programming, relational data, and software development concepts
  • Strong analytical and problem-solving skills
  • Detail-oriented, curious, and willing to learn unfamiliar tools and business processes
  • Strong communication and teamwork skills
  • Available to work 40 hours per week for the full 10-week program
 

Skills Required

  • Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, Software Engineering, Analytics, Engineering, or a related field
  • Basic experience with Python, JavaScript or Node.js, SQL, databases, APIs, Power BI, or cloud technologies through coursework, projects, or previous experience
  • Basic understanding of programming, relational data, and software development concepts
  • Strong analytical and problem-solving skills
  • Detail-oriented, curious, and willing to learn unfamiliar tools and business processes
  • Strong communication and teamwork skills
  • Available to work 40 hours per week for the full 10-week program
Am I A Good Fit?
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The Company
200 Employees
Year Founded: 1993

What We Do

Ragle Inc. is a heavy civil construction company that has operated since 1993. It completes infrastructure projects across Texas, Indiana, Kentucky, and Illinois, specializing in highway and bridge construction as well as deep excavation, grading, and pipe work. The company emphasizes safety, quality, teamwork, and employee support, and maintains an equipment division focused on the reliability and lifecycle management of heavy construction machinery.

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