Data Engineer II
Data Engineer (SW)
Cupertino, CA
12 months
Hybrid (3 days in office/week)
Hours: 40 per week
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
Docker
CI/CD
GitHub
DevOps
Key Requirements
25 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
Cupertino, CA
12 months
Hybrid (3 days in office/week)
Hours: 40 per week
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
Docker
CI/CD
GitHub
DevOps
Key Requirements
25 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
Why TalentBurst?
At TalentBurst, we deliver more than talent, we deliver outcomes. We partner with you to move quickly and connect you to opportunities aligned with your skills and long term growth.
Backed by precision, transparency, and results, we connect top talent with leading organizations through trusted partnerships.
We offer competitive compensation and comprehensive benefits, including medical, dental, vision, and retirement options.
TalentBurst is an equal opportunity employer committed to an inclusive and diverse workforce.
