Staff Software Engineer, Database/Analytics Performance
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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 cross-functional projects in performance engineering for systems.
- Experience building systems that apply statistical analysis or Machine Learning to automate performance diagnostics, anomaly detection, or tuning.
- Experience in mentoring executive engineers, with thought leadership.
- Ability to identify and deliver 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. You will join a team of experts dedicated to optimizing Google's database and analytics infrastructure. You will leverage the experience in performance, distributed systems, and computer architecture to identify and eliminate bottlenecks across the entire stack, from hardware and Operating System (OS) interactions to application reasoning.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.
Responsibilities
- Design, prototype, and implement optimizations in software, system libraries, and distributed algorithms to drive improvements in throughput, latency, and resource efficiency.
- Leverage hardware/software co-design to unlock new performance capabilities, translate the knowledge of modern server architecture into software enhancements.
- Measure the impact of the work through benchmarking, statistical analysis, and production A/B testing to validate performance gains and demonstrate cost savings.
- Lead investigations into performance anomalies and production incidents, performing root-cause analysis that traces issues from application reasoning down to hardware behavior.
- Apply Machine Learning (ML) techniques to automate performance diagnostics and tuning, and architect data systems to efficiently support Machine Learning (ML) workloads from feature engineering to inference.
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