Data Engineering Tech Lead: Large-Scale Analytics

Google; Sunnyvale (CA); On-site; USD 262,000 - 364,000; Full time

Job summary

Google is seeking a Data Engineer Tech Lead to join the Cloud AI Data Engineering team and build the critical data infrastructure, log processing engines, and automated experimentation pipelines powering executive dashboards and guiding product strategy.

You will lead and design robust pipelines for massive datasets, turning raw log signals into real-time metrics while influencing how customers measure their AI investments and how engineering validates feature rollouts.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in languages such as Python, C++, Java, or Go.
• 5 years of experience with design and architecture; and testing/launching software products.
• 3 years of experience with software design and architecture.
• Experience building, debugging, or supporting large-scale data pipelines and data processing systems.

Responsibilities

• Define the technical roadmap and goal for the Gemini Enterprise metrics and analytics ecosystem, aligning with broader Cloud AI product goals.
• Architect scalable batch and streaming pipelines, resolving complex analytics ambiguities and mitigating infrastructure roadblocks before they impact delivery.
• Partner with cross-functional leaders in Data Science, Product Management, and Engineering to identify future metrics requirements and align project timelines.
• Mentor and grow the team, providing guiding feedback and creating opportunities for engineers to stretch into challenging technical responsibilities.
• Ensure system health and enforce SLAs/SLOs for critical production datasets, privacy compliance, and data governance.

Skills

Python

C++

Java

Go

Education

Bachelor's degree or equivalent practical experience

Job description

Google is seeking a Data Engineer Tech Lead to join the Cloud AI Data Engineering team and build the critical data infrastructure, log processing engines, and automated experimentation pipelines powering executive dashboards and guiding product strategy.

You will lead and design robust pipelines for massive datasets, turning raw log signals into real-time metrics while influencing how customers measure their AI investments and how engineering validates feature rollouts.