About This Role
This role blends product judgment with hands-on technical execution, focusing on defining and establishing the company's product analytics and attribution practice. The challenge involves defining the right metrics, linking them to provable outcomes, and ensuring the underlying data can support them. It suits someone who can build alongside the engineering team.
Responsibilities
- Work with each product pod to define their north star and supporting metrics, tied to causal outcomes that can be attributed
- Facilitate agreement on the definition for each metric, identify the data source, and implement it as a durable metric
- Audit existing Datadog RUM events and tagging against needed metrics, spec the correct capture, and partner with engineering to ship it
- Establish a governance checklist so new features launch instrumented
- Design how product usage joins to business outcomes (Salesforce/CPQ: opportunities, renewals, expansion), including the export path from Datadog to a queryable layer
- Prove the attribution model by answering at least one real go-to-market attribution question end-to-end
- Partner with leadership to define how teams will consume product metrics
- Build the right reporting layer, which may include pod-level dashboards, feature-release views, executive rollups, and monitors for key product metrics
- Shape the right outputs rather than follow a preset list, using Datadog, the internal data platform Unity, or both as appropriate
Requirements
- Proven product analytics judgment in B2B SaaS, including defining north star metric frameworks for product teams, distinguishing causal outcome metrics from vanity metrics, and having opinions about activation, retention, adoption, especially in account-based (multi-user-per-customer) products
- Experience facilitating metric definition across product, engineering, and leadership, landing on definitions teams actually adopt and act on
- Experience connecting product usage to business outcomes (attribution to pipeline, renewals, expansion)
- Strong database and data-modeling skills: understanding how events, users, and accounts should be structured, what joins are valid, and when a metric is wrong by construction
- Fluency working with AI tools as a core part of your workflow: directing the work, owning the model, and catching bad output when building dashboards, queries, and pipelines AI-first
- Hands-on Datadog experience beyond ops use: RUM, custom metrics, and comfort scripting against the Datadog API
Nice to have:
- Strong SQL for validating AI-produced work
- Datadog depth: DDSQL, reference tables, cost/cardinality management, and working around the limits of Datadog's product analytics feature set (funnels, cohorts)
