Product Manager - Log Management

About Observe

Observe is re-imagining observability for the cloud era. We deliver open, scalable observability by collecting logs, metrics, traces directly in a cloud data lake, linking every event in a real-time knowledge graph, and using a modern columnar analytics database to query it all. This lake-first architecture eliminates costly upfront indexing, keeps all telemetry in low-cost object storage, and lets engineering teams correlate petabytes of data in real-time. Our customers troubleshoot faster while cutting observability spend by as much as 70 percent.

The role

Log Management is both our fastest-growing workload and the first workload most prospects adopt. As Product Manager for Logs you will own the end-to-end experience: ingest, search, alerting, dashboard, and reporting. Your mission: make Observe the natural upgrade from Splunk, Elastic, Datadog, or home-grown solutions. You will validate problems, align the roadmap, ship furiously, and drive adoption.

This is an in-person/hybrid role in San Mateo, California.

What you’ll do

• Define and maintain a 12-month roadmap for log ingest, search, analytics, alerting, dashboarding, and reporting.

• Validate problem statements through frequent customer interviews, win/loss reviews, and usage data analysis.

• Partner daily with Engineering and Design to scope, ship, and iterate in small, measurable increments.

• Set and track key product and business metrics; adjust priorities based on evidence.

• Drive launches by supplying positioning, demos, and pricing/packaging input to Sales and Marketing.

• Represent the Logs product vision and roadmap internally and externally, aligning stakeholders on direction.

What we’re looking for

• 5+ years of product-management experience in log management, observability, SIEM, or large-scale data platforms.

• Proven experience partnering closely with enterprise B2B go-to-market teams (sales, customer success, marketing).

• Hands-on familiarity with tools such as Splunk, Elasticsearch, or Grafana, and the ability to self-serve basic data analysis.

• Technical depth in distributed systems, cloud storage, or data-lake architectures; comfortable discussing trade-offs with senior engineers.

• Bias for evidence and iteration; thrive in the fast-paced, constantly changing environment of a growth-stage startup.

• Strong written and verbal communication; comfortable with enterprise POCs and technical evaluations.