About
Location: Cupertino, CA (Hybrid)
Duration: 12 months
Role Overview
We are seeking a Data Engineer with strong data pipeline development skills and hands-on experience managing containerized workflows in Kubernetes and Docker. This role combines traditional data engineering responsibilities with infrastructure-focused work to support deployment, monitoring, and automation of data services.
Key Skills:
SQL, Python, Bash/Shell scripting
Spark, Airflow
Snowflake, DBT
AWS S3, Kubernetes
GitHub, Docker
DevOps, CI/CD
Key Requirements:
2-5 years of experience in Data Engineering, Software Engineering, or Analytics
Strong SQL and Python skills with comfort working in Bash/Shell
Hands-on experience with Spark, Airflow, Snowflake, DBT, and AWS S3
Strong Kubernetes and Docker experience, including deploying, managing, and troubleshooting workflows
Familiarity with DevOps practices including CI/CD, monitoring, and automation (AWS preferred)
Ability to bridge both data engineering and infrastructure responsibilities
Solid understanding of data modeling, warehousing, and big data ecosystems
Responsibilities
Build and maintain scalable ELT pipelines using SQL and Python
Deploy, manage, and monitor containerized data workflows in Kubernetes and Docker
Collaborate cross-functionally to deliver reliable and well-documented data solutions
Implement automation and monitoring to improve system performance and reliability
Support urgent reporting requests and ad-hoc data analysis needs
Education:
MS or equivalent experience preferred
Languages
- English
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