Staff Data Engineer, Cloud AI — Scale Big Data Pipelines

Google; United States; On-site; USD 207,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

401(k) with company match

Paid time off

Sick time

Maternity leave

Baby bonding leave

Paid holidays

Job summary

Google Sunnyvale, CA and Kirkland, WA seek a Staff Software Engineer in Cloud AI Data Engineering. You will design scalable pipelines, build data infrastructure, and collaborate with product teams to power AI features at scale.

Lead data governance, secure data handling, and real-time metrics across enterprise workloads, influencing architecture and delivery in a large, matrixed organization.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years programming in C++, Java, Python, Kotlin or Go.
• 5 years testing and launching software products.
• 3 years experience with software design and architecture.
• Experience integrating generative AI tools or LLM interfaces into workflows.

Responsibilities

• Design and optimize highly scalable batch and streaming data pipelines for Gemini Enterprise.
• Develop high-performance data warehousing solutions for search, AI models, and analytics.
• Implement data governance, quality monitoring, and alerting systems for data integrity.
• Enforce data privacy and security policies, manage access controls and data wipeout procedures.
• Partner with Data Scientists, Software Engineers, and Product Managers to define requirements.

Skills

8 years programming in languages

5 years testing and launching software

3 years design and architecture

Experience integrating generative AI

Education

Bachelor's degree or equivalent practical experience

Job description

Google Sunnyvale, CA and Kirkland, WA seek a Staff Software Engineer in Cloud AI Data Engineering. You will design scalable pipelines, build data infrastructure, and collaborate with product teams to power AI features at scale.

Lead data governance, secure data handling, and real-time metrics across enterprise workloads, influencing architecture and delivery in a large, matrixed organization.