Senior PLG Strategy & Operations Manager

Experience owning strategy, growth, or operating initiatives with accountability for business outcomes, ideally in a high-growth SaaS or technology environment. Demonstrated ability to structure ambiguous problems and drive work forward in complex, cross-functional settings. Strong analytical foundation, including comfort working directly with data, designing analyses, and evaluating experiments. Fluency in core analytical and communication tools, including SQL for data exploration, spreadsheet-based modeling, and structured slide development for executive audiences. Executive-level communication skills, including the ability to produce clear, rigorous, and detail-oriented slide materials. Comfort influencing without authority and operating with a high degree of ownership and autonomy.

Own PLG strategy and operating priorities across acquisition, activation, monetization, and high-value customer growth. Lead inputs into annual and quarterly planning, including target setting, scenario analysis, and operating tradeoffs. Partner with senior PLG leadership to drive prioritization, investment decisions, and alignment across initiatives. Own preparation for PLG executive reviews and retrospectives. Translate complex analysis into clear, decision-oriented narratives. Act as a thought partner to senior stakeholders. Partner closely with Product to inform roadmap priorities. Work with Marketing and Sales to shape go-to-market strategy, campaign focus, and execution plans. Diagnose end-to-end performance across acquisition and activation funnels. Establish a structured, hypothesis-driven approach to experimentation and performance improvement. Drive progress across Product, Engineering, Marketing, and Sales by managing dependencies, sequencing, and timelines. Ensure PLG metrics and analyses support leadership decision-making and prioritization. Guide the evolution from volume-based metrics toward value, usage, and customer-level outcomes. Partner with data and engineering teams to ensure analytical rigor and consistent definitions. Own analytical outcomes end-to-end, determining when to go deep personally and when to partner with data science or analytics teams.