Staff Product Manager, Atlas Infinite

The Staff Product Manager is an Individual Contributor (IC) product leader responsible for a broad platform surface for Atlas Infinite at MongoDB. Atlas Infinite is the largest architectural innovation in MongoDB Atlas ever, purpose-built to handle today's speed and scale of development and demand. In this position, you will own and solve end-to-end problems around the user experience and database experience of Atlas Infinite, including but limited to feature discovery, activation, monetization, and retention. You will see patterns, opportunities, and risks across areas, own cross-team product and architectural trade-offs, establish reusable standards for PM craft, and align senior stakeholders around multi-year bets. You have deep knowledge of cloud database platforms, distributed data systems, and enterprise product development. You will partner closely with cross-functional engineering leaders, product peers, and executive stakeholders to drive outcomes across organizational boundaries, guiding a portfolio spanning Atlas Infinite's disaggregated compute and storage architecture, production workload readiness, migration and Atlas interoperability, and the new customer experiences this architecture may make possible. You will not inherit a closed list of future features: part of your mandate is to discover opportunities we have not yet articulated and challenge assumptions about which problems matter most. With AWS public preview now live, you will help turn real workload evidence into a credible path to GA and subsequent releases, in close partnership with the existing team. This role can be based out of a MongoDB office or remotely in Canada. Responsibilities • Interlinked Vision & Multi-Year Bets: Set and champion a product vision across Atlas Infinite's capabilities, while actively discovering opportunities that are not on today's roadmap or fit any single team's remit. Explore what decoupled compute and storage could make possible for developers, enterprises, and emerging AI workloads; use hypotheses, customer evidence, prototypes, and market signals to uncover needs we have not yet named. Branching, on-demand compute, and instant recovery are examples of possible bets. Make the case for investment and prioritization, including build-vs-buy considerations, and distinguish exploratory vision from release commitments • Cross-Team Execution: Along with program management, own execution health across workstreams, negotiating sequencing, scope, and resource trade-offs with engineering leaders and peer PMs where you have no direct authority. Ensure preview-to-GA readiness, roadmaps, and dependencies across database, storage, Atlas platform, migration, backup, security, and cloud infrastructure teams are aligned and connect to company objectives • Execution & Research Standards: Establish lightweight operating practices and shared customer/market research frameworks that make workload qualification, customer-representative testing, win/loss analysis, launch readiness, and post-launch learning consistent across the portfolio. Use data to drive decisions without overburdening delivery teams • Product Health: Resolve complex post-launch escalations when the root cause spans architecture, performance, migration, observability, supportability, or unclear ownership. Turn customer and operational signals into systemic product fixes rather than one-off workarounds • Customer Discovery: Identify needs and competitive dynamics that cut across product areas. Look beyond requests from today's customers to discover emerging jobs-to-be-done, surprising uses of the architecture, and reasons customers do not choose MongoDB at all. Combine interviews, workload and pricing evidence, product analytics, field feedback, and experiments to test