Business Data Analyst II, Google Global Infrastructure, Strategy and Operations

Business Data Analyst II, Google Global Infrastructure, Strategy and Operations

About the job

Google'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...

Minimum qualifications:

• Bachelor's degree in Engineering, Math, Quantitative Science, or a related technical field, or equivalent practical experience.
• 3 years of experience working in data analytics, consulting, data science, engineering, or a technical operations role.
• 3 years of experience in 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.

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.