Senior Software Engineer, Data Engineering, Cloud AI

Position: Senior Staff Software Engineer, Data Engineering, Cloud AI

Minimum 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.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• Experience leading engineering teams, mentoring talent, and architecting solutions for complex data challenges.
• Experience driving cross-functional collaboration and delivering strategic technical recommendations to executive leadership.
• Experience building large-scale analytics platforms and data warehousing solutions using distributed pipeline frameworks.

About the job Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile;

the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will join the Cloud AI Data Engineering team, and build the critical data infrastructure, log processing engines, and automated experimentation pipelines that power executive dashboards and guide product strategy.
As a Data Engineer Tech Lead on this team, you will lead and design robust, compliant pipelines handling massive datasets and transitioning raw log signals into real-time metrics. You will directly influence how our customers measure their AI investment and how our engineering teams validate their feature rollouts.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits ponsibilities

• 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 Service Level Agreements/Service Level Objective (SLAs/SLOs) for critical production datasets, engineering best practices like robust testing, privacy compliance, and data governance.

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