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X The application window will be open until at least October 12, 2026. This opportunity will remain online based on business needs which may be before or after the specified date.Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Austin, TX, USA; Atlanta, GA, USA; Addison, TX, USA; Thornton, CO, USA.
Minimum qualifications: - Bachelor's degree in Engineering, Math, Quantitative Science, or a related technical field, or equivalent practical experience.
- 3 years of experienceworking in data analytics, consulting, data science, engineering, or a technical operations role.
- 3 years of experiencein statistical problem solving and analyzing data sets using SQL or comparable coding language (e.g. Python, R, Java, C ).
Preferred qualifications: - 2 years of experience designing and implementing KPI frameworks, OKR tracking, and headcount management analytics for senior leadership.
- Experience navigating ambiguous business requirements and modeling complex what-if scenarios.
- Experience with data governance best practices and proactively identifying automated solutions for data quality improvements.
- Experience leveraging Generative AI (GenAI) or Large Language Model (LLM) tools to accelerate analytical workflows, deep-dive trend analyses, or automated reporting.
About the jobGoogle's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.
As a Business Data Analyst, you will act as the analytical backbone for Google Global Infrastructure (GGI) leadership, translating complex operational data into easily digestible insights and strategic narratives. Your role is crucial for elevating the way GGI performs at scale, empowering executive decision-making across Key Performance Indicator (KPIs), Objectives and Key Results (OKRs), Top Programs and headcount management.The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're behind Google's groundbreaking innovations, empowering the development of AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $124000 - $177000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Own and execute end-to-end data preparation, analytical modeling, and metric validation for regular reports, and translate complex operational data into actionable strategic narratives for stakeholders.
- Conduct deep-dive trend analyses across core data areas (such as headcount management, program management, OKRs, and KPIs) to provide proactive, automated alerts and insights to senior leadership.
- Partner closely with StratOps leads to provide analytical tracking frameworks, status reporting, and performance insights for core business areas.
- Evaluate regularly and provide recommendations to improve overall data quality.
- Enable the CoS office with data models and what-if scenarios to support leadership in making operational decisions.
