Site Reliability Engineer Intern, Data Infra (Spring/Summer 2026)

About the team

We are looking for a proactive and detail-oriented Site Reliability Engineering (SRE) Intern to join our Big Data Infrastructure team. This internship is ideal for students who are passionate about Linux systems, scripting, and large-scale data platforms. You will gain hands-on experience in operating and improving the reliability of data infrastructure services.

 

What you'll do

• Support daily operations of big data platforms, including monitoring, troubleshooting, and routine maintenance.
• Write and optimize Shell scripts to automate operational workflows and system tasks.
• Assist in system health checks, log analysis, and reliability improvements.
• Participate in building tools to enhance the observability and automation of data services.
• Document standard operating procedures and support knowledge sharing across the team.

Requirements

Basic Qualifications 

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• Strong understanding of Linux operating systems and command-line tools.
• Proficiency in Shell scripting (bash, sh, etc.).
• Clear interest in large-scale systems and reliability engineering.
• Willingness to learn, take initiative, and work collaboratively.

 

Bonus Qualifications (Nice to Have)

• Familiarity with Python for automation or internal tooling.
• Experience with web platform development (e.g., using Flask, FastAPI, or similar frameworks).
• Exposure to big data and storage engines such as: HDFS, Apache Ozone, Alluxio
• Understanding of monitoring or alerting tools (e.g., Prometheus, Grafana, ELK).
• Knowledge of Git and basic CI/CD workflows.

 

What You'll Gain

• Real-world experience in operating and improving a production-grade big data platform.
• Exposure to SRE practices including automation, fault-tolerance, and observability.
• Mentorship from experienced infrastructure engineers.
• Potential opportunity for full-time conversion based on performance and graduation timeline.