Data Engineering Intern — Build Scalable Data Pipelines (Summer)

Energy Jobline ZR

Waller (TX)

On-site

USD 34,000 - 48,000

Full time

5 days ago
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Job summary

Daikin’s 11-week summer program invites students to contribute to meaningful projects while receiving mentorship, training, and collaboration. Interns gain hands-on experience in a thriving industry with strong opportunities for growth.

As a Data Engineer intern, you will build scalable data pipelines, work with SQL and AWS data technologies, and collaborate with cross-functional teams to improve data reliability and efficiency.

Qualifications

  • Currently enrolled as a student and eligible for summer internship.
  • Graduation dates: December 2027 - May 2029.
  • Be at least 18 years of age.

Responsibilities

  • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS big data technologies.
  • Work with stakeholders including the Executive, Manufacturing, Sales and Marketing teams to assist with data-related technical issues and support their data infrastructure needs.
  • Work with data and analytics experts to strive for greater functionality in our data systems.
  • Develop ways to improve data quality, reliability, and efficiency.

Skills

SQL
AWS
Data engineering
Data quality

Education

Pursuing Bachelor's degree in a related field

Tools

AWS big data services
ETL frameworks

Job description

Daikin’s 11-week summer program invites students to contribute to meaningful projects while receiving mentorship, training, and collaboration. Interns gain hands-on experience in a thriving industry with strong opportunities for growth.

As a Data Engineer intern, you will build scalable data pipelines, work with SQL and AWS data technologies, and collaborate with cross-functional teams to improve data reliability and efficiency.

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