SiteOps Global Operations Analytics Engineer

Salary

$124,000/year to $176,000/year + bonus + equity + benefits

Location

Menlo Park, CA; Fremont, CA

Meta is seeking a Global Operations Analytics Engineer to join our Data Center Site Operations ("SiteOps") team. Our data centers are the foundation upon which our rapidly scaling infrastructure efficiently operates and upon which our innovative services are delivered.

The Global Operations Analytics Engineer leads the development of an analytics data set and use cases to support and enable the effective and efficient use of the global server fleet. As we grow our data center fleet to support Meta’s products, we need to leverage data and insights to drive decisions that enable predictability, consistency, and proactive actions to allow us to operate efficiently and reliably at scale. The role will be responsible for creating end-to-end operational analytics and insights, from data sourcing to surfacing insights and driving action, for various aspects of Meta’s global data center operations.

The Global Operations Analytics Engineer also plays a crucial role in translating data to action and identifying efficiency and effectiveness opportunities. This individual uses data to provide meaningful recommendations and actionable strategies to key stakeholders, and develop best practices, including streamlining of data sources and related programmatic initiatives. Join us if you have a passion for working in white space and creating impact from the ground up in a fast-paced environment.

SiteOps Global Operations Analytics Engineer Responsibilities

Acts as key data expert on operation of servers in a hyper-scale data center environment
Formulate the right metrics and definitions of success to drive quality, efficiency, cost, and timeliness understanding the source data
Perform complex data analysis leveraging data streams available to drive proactive and predictive action.
Align and support the global team behind these metrics, and be a key driver ensuring rapid, coordinated, and effective responses to issues as they arise
Work with partners to use data mining and statistical techniques to validate or identify operational inefficiencies, exceptions, and fault/event correlation
Bring multiple areas of business and engineering together via a common reliable foundation of common data, metrics, and insights to enhance operational performance of Meta's infrastructure
Provide data supplied narratives, determining what is important and what next steps should be taken
Build data pipelines, tables and dashboards, and leverage these for interpreting incidents and trends
Leverage knowledge and expertise in Operational processes to partner with data engineers and software engineers to ensure high quality and reliable data is available for analysis
Lead and support various ad hoc projects, as needed, in support of Meta’s Data Center strategy
Become an expert in Meta’s site operations infrastructure, tools, systems, and data
Leverage tools like R, Tableau, Python, and SQL to drive efficient analytics

Minimum Qualifications

Degree in an analytical field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research, Data Science, Management Science or commensurate experience)
3+ years of experience in a role with data analysis and metrics development
3+ years of SQL development experience creating queries
Experience with data visualization tools
Knowledge of leveraging data driven models to drive business decisions
Experience using data access tools and building visualizations using large datasets and multiple data sources
Experience communicating operational data insights to all organizational levels
Knowledge of statistics (e.g. statistics basics, statistical modeling, experimental design, hypothesis testing, etc.)

Preferred Qualifications

Experience working in a large-scale data center environment
Experience with multiple coding languages (R, Hack, Python)
Hands-on experience analyzing and interpreting data, drawing conclusions, defining recommended actions, and reporting results across stakeholders

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