Strategy and Operations Analyst Lead, Go-To-Market

Google•Mountain View, CA

About The Position

The Business Strategy & Operations organization provides business critical insights using analytics, ensures cross functional alignment of goals and execution, and helps teams drive strategic partnerships and new initiatives forward. We stay focused on aligning the highest-level company priorities with effective day-to-day operations, and help evolve early stage ideas into future-growth initiatives. As the Central StratOps & Insights team, we serve as the force that catalyzes ALCS product activation strategies by providing a cross-product objective view on opportunity, building and executing scaled approaches with excellence, and equipping the business with untapped insights. We partner cross-functionally and serve as the core function bringing together ALCS product and seller activation approaches. In this role, you will collaborate closely with pGTM product leads and their respective teams, as well as other cross-functional stakeholders. The objective is to be the driving force behind data-led growth for our Americas LCS and pGTM teams. You'll design, build, and optimize data infrastructure to deliver actionable insights at scale, directly empowering smarter strategic decisions. The Go-to-Market Operations (GtM) team ensures Google's complex and ever-evolving Ads business runs smoothly. We are instrumental in setting go-to-market strategy, and ensuring flawless execution and operations against the strategy. We have teams embedded in each of the major Ads business areas as well as global teams that work across the business areas. Team members are analytical and strategic, with a pragmatic sense of how to get things done. The US base salary range for this full-time position is $148,000-$215,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google [https://careers.google.com/benefits/].

Requirements

Bachelor's degree in Engineering, Math, Quantitative Science, or related technical field, or equivalent practical experience.
6 years of experience working in data analytics, consulting, data science, engineering, or a technical operations role.
6 years 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.) or MBA.
6 years of experience with designing analysis, developing models, and identifying business opportunities using data driven insights.
6 year of experience working with Sales, Go-to-Market, or StratOps teams.
Experience in data analysis.
Ability to solve problems in complex, fast-moving, and ambiguous business environments through solid data intuition and business acumen.

Responsibilities

Execute in-depth analyses across multiple data-sets (e.g., revenue, product adoption, operations, headcount data) to identify opportunities for growth using advanced SQL and create holistic views on investment opportunities for the business.
Design, develop and launch scaled insights solutions (e.g., dashboards, AI-powered summaries) to enable stakeholder teams to independently and consistently track and manage key metrics.
Be a strategic thought partner to broader pGTM through effectively developing and communicating data driven insights and recommendations, and partner with cross-functional teams to drive tangible outcomes.
Present clear and compelling data-backed stories to senior leadership, influencing strategic direction and business outcomes.
Leverage AI technology to optimize current workflows and identify key insights. Identify opportunities to upskill the broader team on analytics and data infrastructure knowledge and capabilities.