To apply for this job, you need to complete both steps below:
STEP 1:
Please click the link to submit your application directly to the company: 

ShopBack - Data Engineering Intern

Your application will only be received by Recruiter if submitted via above link. 

STEP 2:
Kindly scroll to the bottom of this page and complete the short VinUni Tracking Form.

Filling out this form alone does not count as applying. Kindly remind this form is not part of the company’s application process. It only helps Careers, Alumni, Industry and Development (CAID) Department discover more opportunities and follow up in case of system issues.

Our Data Platform team builds and maintains the foundation that powers ShopBack’s analytics and decision-making.

We design and operate data pipelines across AWS S3, Apache Iceberg, and Trino, orchestrated through Airflow and modeled via dbt-on-Spark. You’ll work alongside experienced data engineers who value clean data, efficient systems, and thoughtful design, not just working code.

Your Adventure Ahead

• Build and maintain data models and pipelines using Spark and Apache Airflow
• Learn to design and optimize HUDI and Iceberg tables for performance and reliability
• Write and validate SQL transformations consumed in Trino and Metabase
• Collaborate with senior engineers to improve data quality and observability
• Use AI tools (e.g., ChatGPT, Cursor, Claude Code) to assist coding and documentation, and learn how to verify their output
• Document learnings and share improvements through Confluence and Slack
  
  
  

What You Will Learn

• How modern data lakehouses (Spark + Iceberg + Trino) work in production
• Building reliable, testable dbt models on top of large datasets
• End-to-end flow: ingestion → transformation → analytics
• Practical debugging, observability, and version control in a real system
• How to collaborate effectively in a hybrid engineering team
  
  
  

Essentials To Succeed

• Strong interest in data engineering or data systems
• Familiarity with Python and SQL (school projects or self-taught is fine)
• Curiosity about data pipelines, storage formats, and data quality
• Comfort experimenting with AI coding assistants responsibly
• Clear communication, asks questions early, shares progress regularly
• Bonus: exposure to AWS, dbt, Spark, or Trino