Product & Design

Product Operations Career Resources

Process design, tool adoption frameworks, and product analytics storytelling for Product Ops professionals.

Bottom line

Start with the decisions that need to be made, not the data you have. Every metric should have an owner, a trigger for action, and a next step — otherwise it's decorative.

40%

Of product decisions at high-growth companies are made without sufficient data, per survey

ProductPlan research
22%

Average activation improvement when drop-off root causes are identified and addressed

Askia client data
$138K

Median base salary for Senior Product Operations managers at growth-stage companies

Industry data

Is this track right for you?

Use this track If you…

  • You're establishing or improving product analytics infrastructure for a product team
  • Your team has data but struggles to translate it into product decisions
  • You're building launch health monitoring for a new product or feature

Consider another track If you…

  • You're a data engineer focused on pipeline infrastructure rather than product insights
  • Your team already has a mature analytics-to-decision workflow
  • You're targeting a pure business intelligence role

Common questions

What analytics tools should product ops own vs data team?

Product ops should own the product analytics layer (Mixpanel, Amplitude, Heap) and the dashboard layer. Data engineering owns the pipeline and warehouse. The handoff point is the clean event schema — product ops defines what events to capture, data engineering implements the capture.

How do I get PMs to actually use analytics dashboards?

Build dashboards with PMs, not for them. One working session where the PM defines the questions they need answered produces a more useful dashboard than any independently-built reporting suite.

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