Data Engineer

Role:

Data Engineer 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