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

Google•Atlanta, GA

2d•$102,000 - $145,000

About The Position

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. 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 part of the Google Global Infrastructure (GGI) team, you will oversee the physical infrastructure powering and connecting all of Google’s consumer services, AI services, and Cloud services globally. Your team is responsible for the strategy, operations, technology roadmap, delivery, and optimization for Google’s global footprint of data centers, energy and network infrastructure, across design, power, space, and fleet-wide demand planning. As a Business Data Analyst, you will play a crucial role in supporting our Strategy & Operations (StratOps) team by transforming complex operational data into actionable insights. In this role, you will execute end-to-end data preparation, maintain critical reporting deliverables, and support deep-dive analyses across core data areas. You will ensure strict data integrity and assist with "what-if" scenario modeling, your work will directly empower senior leadership and the Chief of Staff office to make informed, data-driven decisions. 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.

Requirements

Bachelor's degree in Engineering, Math, Quantitative Science, or a related technical field, or equivalent practical experience.
1 year of experience working in data analytics, consulting, data science, engineering, or a technical operations role.
1 year of experience in statistical problem solving and analyzing data sets using SQL or comparable coding language (e.g. Python, R, Java, C++).

Nice To Haves

Master's degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Mathematics, Data Science, etc.).
2 year of experience with designing experiments, developing statistical models, measurement, and identifying business opportunities.
Experience executing against defined business requirements and assisting in data modeling.
Experience applying data governance best practices to identify and correct data quality issues.
Experience leveraging Generative AI (GenAI) or Large Language Model (LLM) tools for analytical workflows, data summarization, or reporting.
Ability to effectively communicate analysis and recommendations across a wide range of non-technical audiences.

Responsibilities

Execute data preparation, metric validation, and routine updates to maintain automated alerts and reporting dashboards.
Support deep-dive analyses across core data areas, including headcount management, program management, Objectives and Key Results (OKRs), and Key Performance Indicator (KPIs).
Conduct regular data quality checks to identify anomalies, flag inconsistencies, and implement corrections that ensure ongoing data integrity.
Assist in building "what-if" scenarios and data models to support senior team members in day-to-day operational decisions.
Translate operational data into clear updates and dashboards to influence business decision-making and present insights to stakeholders.