Staff Product Manager

About the role

The observability market is shifting from surfacing data to delivering answers and MongoDB is seeking a Staff Product Manager to lead that shift for database users, owning the strategy that takes customers from raw telemetry to AI-powered diagnostics and autonomous remediation, so developers can focus on building great applications instead of managing infrastructure. You will define how MongoDB turns raw telemetry into actionable intelligence, set the vision for AI-powered root cause analysis and proactive recommendations, and shape what best-in-class database observability looks like at global scale. The ideal candidate has experience shipping products that deal with data at scale, can engage deeply with senior engineering on technical architecture, and knows how to balance long-term platform investment against near-term customer value while speaking to candidates who are based in Dublin for our hybrid working model. You will operate at the intersection of product strategy, platform scalability, and customer outcomes in a market where speed of insight directly translates into competitive advantage.

Key facts

What you'll do

Define the vision, strategy, and multi-year roadmap for MongoDB observability, balancing the needs of developers, ops teams, enterprise customers, and internal engineering teams to create a cohesive long-term direction.

Help shape the next generation of AI-powered and agentic observability, including intelligent anomaly detection, automated root cause analysis, and proactive recommendations that help customers resolve issues before they escalate to critical incidents.

Own product strategy for experiences that span database health, performance diagnostics, alerting, log analysis, and data visualization, creating a coherent observability journey rather than a collection of disconnected tools that confuse users.

Identify high-impact opportunities across the observability stack, from how customers monitor and understand their deployments to how MongoDB can reduce the operational burden of managing a database fleet at scale while preserving reliability.

Lead customer discovery with developers, DBAs, and enterprise teams; turn qualitative and quantitative insights into clear product decisions that reduce time spent managing the database and increase time spent building applications that drive business value.

Partner deeply with engineering and design to frame problems, define requirements, make tradeoffs, and deliver high-quality products from discovery through launch and iteration, ensuring technical feasibility and customer desirability align.

Establish clear success metrics for observability, using product analytics, customer feedback, research, and market signals to evaluate progress and adjust priorities in a data-driven manner.

Communicate product direction and decisions clearly to senior leaders and cross-functional stakeholders, including the rationale behind what we will and will not build to maintain alignment and trust.

Raise the bar for product management by mentoring other PMs, improving product practices, and modeling strong judgment, customer empathy, and execution across the entire observability portfolio.

Translate complex database telemetry concepts into narratives that resonate with both technical practitioners and executive sponsors, enabling informed investment decisions.

Champion experimentation and learning loops within the observability experience, validating hypotheses through prototypes, user testing, and live data to refine product-market fit.

Balance platform investments against immediate customer pain points, ensuring that foundational capabilities support both current users and future innovation in database operations.

Collaborate with sales, support, and success teams to surface real-world usage patterns and unmet needs, feeding these insights back into the product lifecycle.

Drive alignment across the broader product ecosystem, ensuring observability integrates smoothly with monitoring, APM, and incident management workflows used by customers daily.

Requirements

10+ years of product management experience; including experience leading complex technical products in the data management or analytics space to demonstrate depth in this domain.

A track record of defining strategy and delivering products used by technical audiences, ideally across database, data infrastructure, or observability to show relevant context and credibility.

Experience translating ambiguous customer and business problems into focused strategies, product narratives, roadmaps, and prioritized execution plans that account for technical constraints and market dynamics.

Demonstrated ability to lead through influence across multiple engineering and product teams without relying on formal authority, fostering collaboration in matrixed environments.

Excellent customer instincts and the ability to move comfortably between user research, product details, business strategy, and executive communication to maintain a customer-obsessed mindset.

Experience launching products that require behavior change, ecosystem coordination, or adoption across both self-serve and enterprise customers to navigate complex buying and usage patterns.

Comfort working with AI-enabled product experiences and evaluating where automation, guidance, and human control create the most value in observability workflows.

Exceptional written and verbal communication skills, with the ability to make complex technical concepts clear and compelling for diverse stakeholders.

A bias toward action, disciplined prioritization, and the judgment to make decisions with incomplete information while maintaining accountability for outcomes.

Nice to have

Familiarity with observability tools and platforms such as CloudWatch, Datadog, Grafana, Splunk, OpenTelemetry to understand existing paradigms and user expectations.

Experience building or scaling telemetry infrastructure, data pipelines, or platform products used by other engineering teams to ensure robust and scalable data foundations.

Familiarity with MongoDB, Atlas, or comparable database products and the operational challenges customers face managing them at scale to speak knowledgeably about customer contexts.

Understanding of AI and machine learning concepts relevant to observability, including model evaluation, prompt engineering considerations, and implications for product behavior.

Experience contributing to open source observability projects or engaging with developer communities to understand emerging practices and expectations.