Description
Joining Razer will place you on a global mission to revolutionize the way the world games. As an AI Data Engineer Intern, you will collaborate closely with data engineers, data scientists, analytics engineers, and data product managers to design, optimize, and scale modern data pipelines that fuel AI models, business intelligence, and data-driven decision-making across the organization.
This role sits at the intersection of data engineering, cloud infrastructure, and artificial intelligence. You will gain hands-on experience with real production systems, modern data stack technologies, and create AI-enabled workflows in a cloud-native environment.
By the end of the internship, you will gain practical experience in building AI-ready data systems using industry best practices and modern data technologies.
Responsibilities:
- Collaborate with data engineers, data scientists, analytics engineers, and data product managers to design, optimize, and scale modern data pipelines.
- Gain hands-on experience with real production systems, modern data stack technologies, and create AI-enabled workflows in a cloud-native environment.
- Participate in the development of industry best practices and modern data technologies.
Pre-requisites:
- Strong interest in Data Engineering, Cloud Technologies and AI/ML applications.
- Proficiency in Python, SQL.
- Exposure to cloud technologies (Amazon Web Services, Google Cloud Platform).
- Exposure to orchestration tools (Airflow).
- Exposure to business intelligence tool (Superset, PowerBI, Tableau).
- Ability to work independently and collaboratively in a fast-paced environment.
Learning Objectives:
- Modern Data Warehousing & Lakehouse Architecture in the cloud (AWS).
- Pipeline Design Patterns using ELT.
- Data transformation tools (DBT).
- Infrastructure as Code (Terraform).
- Workflow orchestration (Airflow).
- CI/CD for Data Engineering.
- Spec driven development for data engineering.
- Performance Optimization & Data Quality Engineering.
- Documentation and communication of technical solutions.
- Collaboration with data engineers, analytics engineers, data analysts, data scientists.