Global Operations Analytics Engineer

Meta is seeking a Global Operations Analytics Engineer to join our Data Center Site Operations team. Our data centers are the foundation upon which our rapidly scaling infrastructure efficiently operates and upon which our services are delivered.The Global Operations Analytics Engineer leads the development of analytics data sets and use cases to support and enable the effective and efficient use of Meta's global server fleet. As we grow our data center fleet to support Meta's products, this role leverages data and insights to drive decisions that enable predictability, consistency, and proactive actions, allowing us to operate efficiently and reliably at scale.You have a demonstrated understanding of AI's business applications and the experience to identify, scope and deliver AI-enabled and automated solutions that improve operational efficiency, and data insights generated by the analytics team. While the you won't be building AI models, you will be involved in the design, development, and deployment of intelligent solutions that reduce the operational burden of identifying analytical insights and enabling us to focus on strategic, high-value projects. This role will directly impact the scalability and efficiency of our operations through proactive identification, automation, and resolution of routine tasks, inefficiencies, and incidents.In addition, this is a crucial role in translating data into action and identifying opportunities for efficiency and effectiveness. You are expected to provide meaningful insights into operational performance and make recommendations and actionable strategies to key stakeholders. You love exploring white space and creating impact from the ground up in an ever changing environment, with a demonstrated track record of thought leadership and impact in developing analytics and metrics-based programs.

Global Operations Analytics Engineer Responsibilities:

• Translate business challenges into clear, actionable requirements for AI-enabled solutions
• Map end-to-end business processes, highlighting areas where AI can drive efficiency and value
• Design, build and implement AI-driven automation solutions to uncover operational data insights that drive decision making
• Develop and deploy solutions and AI prompts to identify and address operational bottlenecks, replacing manual interventions with intelligent automation
• Create scalable automation mechanisms that proactively monitor, analyze, and report on operations
• Build robust predictive models using statistical and machine learning techniques to forecast risks, anticipate issues and optimize operations
• Surface actionable insights that enable data-driven, proactive decision-making in global operations
• Develop monitoring tools to trigger early warnings and facilitate rapid resolution through automation
• Acts as a data subject matter go to on operation of servers in a hyper-scale data center environment
• Formulate the right metrics, measures, 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
• Partner with operational analysts and data science to understand pain points, identify repetitive tasks and translate them into opportunities for automation
• 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 that are connected to the business, determining what is important and what next steps should be taken
• Build data tables and dashboards, and leverage these for interpreting incidents and trends
• Lead and support various ad hoc projects, as needed, in support of Meta's data center strategy
• Leverage tools like R, Tableau, Python, and SQL to drive efficient analytics
• Travel up to 30-49% required

• Degree in an analytical field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research, Data Science, Management Science or commensurate experience)
• 7+ years of experience in analytics, engineering, use of AI and/or workflow automation for business improvement
• 7+ years of SQL development experience writing queries, R or Python
• Hands-on experience with AI/ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn) and automation tools/platforms
• Significant experience with data visualization tools and leveraging data driven models to drive business decisions
• Experience with statistics (e.g. statistics basics, statistical modeling, experimental design, hypothesis testing, etc.)
• Demonstrated experience with proactively identifying, scoping and implementing solutions
• Hands-on experience analyzing and interpreting data, drawing conclusions, defining recommended actions, and reporting results across stakeholders

• Experience working in a large-scale data center environment
• Leadership and/or team management experience
• Experience designing measurement systems to assess large scale performance