Product Manager II, Log Management
DatadogNew York, New York, USAPosted 13 March 2026
Job Description
About Datadog:
We're on a mission to build the best platform in the world for engineers, operations, and security teams to understand, secure, and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day, allowing for seamless collaboration and problem-solving among DevOps and Security teams globally for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way.
The Team:
Logs are one of the richest sources of operational, security, and business data: powering use cases such as troubleshooting, threat detection, high volume log optimization, business transaction monitoring, and compliance. We're looking for a Product Manager II to help build the next generation of AI-powered log management capabilities. Our vision is to make it radically faster and easier for engineers, operators, and security teams to ingest, explore, and extract actionable insights from petabytes of log data using AI-first experiences.
You'll work across the log lifecycle, from ingestion and processing pipelines to exploration and analytics, partnering with engineering, design, and data science to ship features that leverage agentic AI, natural language querying, and intelligent automation to transform how customers interact with their logs.
At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.
What You'll Do:
Develop a deep understanding of the day-to-day life of infrastructure, operations, and security teams that rely on logs to measure the health, performance, and security posture of their systems.
Identify and prioritize high-impact use cases where AI-driven log analytics can deliver outsized customer value, such as intelligent log pattern detection, anomaly identification, AI-assisted root cause analysis, and natural language querying.
Define and build experiences that combine Agentic AI, Workflows, and Analytics to create next-generation investigation and remediation runbooks for operations and security teams.
Collaborate with engineering, design, and data science teams to build and iterate on AI-driven insights, automation, and workflow applications on top of log data.
Drive customer adoption and engagement, working cross-functionally with GTM teams to bring these capabilities to market.
Drive thought leadership by creating blog posts, webinars, and conference talks that showcase Datadog's AI-powered log management capabilities.
Who You Are:
A Bachelor's Degree in Computer Science or Engineering or equivalent experience.
3+ years of product management experience in AI/ML-powered product features (such as anomaly detection, NLP-based search, or agentic AI workflows), analytics, observability, or infrastructure.
A proven track record as a technical product manager independently defining strategy and owning execution of a SaaS product or data platform.
You are an AI-native product manager, you actively use AI coding tools (e.g., Claude Code, Cursor, etc.) to prototype, analyze data, and accelerate your own productivity. You have a GitHub that demonstrates what you've built using these tools.
Highly motivated to hit the ground running with a strong sense of ownership and independence. Strong problem-solving, communication, and customer-centric mindset.
Bonus Points:
Experience in Log Management, observability, or analytics platforms (e.g., Splunk, Elastic, ClickHouse, Tableau, Looker).
Experience with databases, data warehouses, or SQL-based analytics at petabyte scale.
Familiarity with major cloud providers (AWS, Azure, GCP) and concepts like cloud-native logging, log routing, and archival strategies.
Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. T ... (truncated, view full listing at source)
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