Staff Software Engineer, Database/Analytics Performance

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products.
• 5 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
• 3 years of experience with software design and architecture.
• Experience with performance analysis, and computer architecture.

Preferred qualifications:

• PhD in Computer Science or a related field with a focus on computer architecture, operating systems, or distributed systems.
• 6 years of experience leading complex, cross-functional projects in performance engineering for planet-scale systems.
• Experience building systems that apply statistical analysis or Machine Learning to automate performance diagnostics, anomaly detection, or tuning.
• Experience mentoring executive engineers, with expertise and thought leadership in a relevant domain.
• Ability to identify and deliver novel, high-impact optimizations through hardware/software co-design.
• Ability to influence and drive technical roadmaps and architectural decisions across multiple engineering organizations.

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will help drive improvements in performance, reliability, and efficiency for Google's data pillar applications through cross-stack optimizations spanning multiple layers of the computing stack and leverage learnings and expertise to guide fleet hardware/software optimizations and designs.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design, prototype, and implement optimizations in low-level software, system libraries, and distributed algorithms to drive substantial improvements in throughput, latency, and resource efficiency.
• Leverage hardware/software co-design to unlock new performance capabilities, translate your deep knowledge of modern server architecture into targeted software enhancements.
• Rigorously measure the impact of your work through benchmarking, statistical analysis, and production A/B testing to validate performance gains and demonstrate cost savings.
• Lead deep-dive investigations into complex performance anomalies and production incidents, performing root-cause analysis that traces issues from application logic down to hardware behavior.
• Apply Machine Learning (ML) techniques to automate performance diagnostics and tuning, and architect data systems to efficiently support large-scale ML workloads from feature engineering to inference.