Minimum qualifications:
- Bachelor's degree in Electrical, Process, or Manufacturing Engineering, or equivalent practical experience.
- 8 years of experience as a Manufacturing, Quality, Reliability, or Product Engineer.
- 5 years of experience in full-stack enterprise or web application development.
- 5 years of experience in development of databases, ETL, SQL, Analytics, and Machine Learning.
- Experience in hardware quality and reliability domains.
- Excellent Communication Skills.
About the job
In this role, you will deliver timely and effective insights for improving data center hardware quality. You will deliver insights distilled from a wide variety of data across Google's data centers and supply chain.
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Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.
The US base salary range for this full-time position is $134,000-$198,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 and implement advanced analytics and machine learning models.
- Own data engineering workstreams, including data acquisition, data movement, data quality, and database design and tuning.
- Develop a custom analytics web app, including APIs, user-interface, and interactive visualizations.
- Engage stakeholders to gain an understanding of root business needs and ensure these needs are met as efficiently and timely as possible.
- Incubate, prototype, and iterate in this space to bring new ideas to production.