Remote Data Infrastructure Engineer Intern

Sezzle

Town of Peru (WI)

On-site

USD 8,100 - 9,700

Part time

11 days ago

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Job summary

Sezzle is seeking a Data Infrastructure Engineer Intern to join a fast-paced, data-driven team. You’ll help maintain our data warehousing stack and support data pipelines while learning the latest in cloud data engineering.

This role emphasizes hands-on learning, collaboration, and the application of AI tools to accelerate development and improve reliability. You will work with Golang, Python, and SQL to build scalable data solutions, gaining exposure to AWS, Redshift, and streaming concepts in

Qualifications

  • Pursuing or recently completed degree in CS, data engineering, or related field.
  • Basic SQL and NoSQL familiarity.
  • Experience with Claude or similar LLM tools.
  • Willingness to learn and work in a collaborative team.

Responsibilities

  • Assist in maintaining and improving our data warehousing systems (AWS DMS, Redshift).
  • Support data pipelines monitoring, identify issues, and troubleshoot basic problems.
  • Write and optimize SQL queries for analytics and data validation.
  • Collaborate with engineers and analysts to understand data needs and use cases.
  • Participate in testing, documentation, and reliability improvement of data systems.
  • Identify and automate repetitive daily tasks to reduce operational overhead.
  • Learn and explore new tools and technologies in the data ecosystem.

Skills

Python
Go
SQL

Education

Pursuing CS/Data Engineering degree

Tools

Claude

Job description

Sezzle is seeking a Data Infrastructure Engineer Intern to join a fast-paced, data-driven team. You’ll help maintain our data warehousing stack and support data pipelines while learning the latest in cloud data engineering.

This role emphasizes hands-on learning, collaboration, and the application of AI tools to accelerate development and improve reliability. You will work with Golang, Python, and SQL to build scalable data solutions, gaining exposure to AWS, Redshift, and streaming concepts in

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