At Google, data drives all of our decision-making. As part of the Finance Business Intelligence team, you will use data to inform business and product decisions across the company. Using your technical skills, business acumen and creativity, you will build tools to automate reporting and generate insight that will allow clients to quickly and accurately see how our key business products and processes are performing. Previous experience managing finance systems projects, with emphasis on providing business intelligence and data driven insight through reporting, will have equipped you well for this role. You’ll be involved in projects from inception to delivery, ensuring that your reporting delivers high-quality and relevant data to intelligently grow our business. You will also have extensive knowledge of key financial systems, such as Oracle and Hyperion, and experience working with a range of other business intelligence tools and platforms. The Cloud Finance BI team supports the broader Cloud Finance organization and Cloud senior leadership. The team owns the financial analytics and automation roadmap and works with Finance and Engineering partners to build stable reporting, forecasting, simulation and modeling assets. Our goal is to enable decision intelligence while reducing the reporting burden on analysts. In this role, you will control the Cloud P&L, drive modeling, forecasting and automation efforts and lead data science initiatives to advise on optimal HC resource allocation and drive accountability. Your deep business knowledge, data mastery, and sharp insight provide us a seat at the table as essential partners and strategic advisers to the Cloud leadership team. 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: $148000 - $214000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities • Take ownership of designing, developing, and maintaining critical data assets for Cloud Finance, including data marts, pipelines, and dashboards. Build an insightful, accurate, and production-level financial reporting infrastructure. • Develop scalable ML and AI modeling solutions for anomaly detection and forecasting to surface insights at scale, bringing clarity and new analytical capabilities to complex financial trends. • Seek out opportunities to improve our financial operations by devising new or automating existing financial models and processes, statistical or driver-based forecasts and simulations. • Serve as a trusted and reliable partner to Finance colleagues across Alphabet. Support their processes and empower them with new tools and uplevel their analytical capabilities. • Deliver insights to decision-makers which includes automating insight-gathering processes and owning the communication of complex technical analyses to senior audiences such as Finance VPs, Directors, and Chiefs of Staff. Minimum qualifications: • Bachelor's degree in a quantitative discipline or equivalent practical experience. • 7 years of experience in business intelligence, analytics, or quantitative analysis related fields. • Experience programming with SQL, and an additional programming language (e.g. with R, Python, JavaScript). Preferred qualifications: • Master's in a technical or quantitative field, or equivalent practical experience. • Experience building data infrastructure and solutions (e.g. designing architecture, building big data pipelines, implementing scalable monitoring). • Experience building Machine Learning models for forecasting or anomaly detection purposes. • Proficiency in a programming language for data analysis and automation (e.g., Python or R). • Excellent communication skills, including the ability to communicate complex findings in a concise manner to a non-technical audience.