Financial Analyst, Cloud Product Finance

GoogleSunnyvale, Chicago, Kirkland, California, Illinois, WashingtonOn-siteFull-timeSenior, 5–8 yearsListed 41 minutes ago

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About this role

Financial Analysts ensure that Google makes sound financial decisions. As a Financial Analyst, your work, whether it's modeling business scenarios or tracking performance metrics, is used by our leaders to make strategic company decisions. While working on multiple projects at a time, you are focused on the details while finding creative ways to solve big picture challenges.

The name Google came from "googol," a mathematical term for the number 1 followed by 100 zeros. And nobody at Google loves big numbers like the Finance team when providing in depth analysis on all manner of strategic decisions across Google products. From developing forward-thinking analysis to generating management reports to scaling our automated financial processes, the Finance organization is an important partner and advisor to the business.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $141000 - $205000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

Minimum qualifications:

- Bachelor's degree in Business, Finance, Economics, Statistics, or another
quantitative field, or equivalent practical experience.

- 7 years of experience in financial planning and analysis (FP&A),
consulting, or a related function, or 5 years of experience with a relevant advanced
degree.

Preferred qualifications:

- Experience using analytics to solve product or business issues.

- Proficiency in using various data related tools including SQL and querying databases.

- Ability to adopt AI tools to make the data analysis efficient and timely.

- Develop data driven solutions and visualizations for capacity planning and cost related metrics.

- Provide financial reporting and insights working through large datasets and building data pipelines across disparate sources.

- Act as a key enabler for explaining variances and margin changes across cycles through data.