Product Operations Manager

Here's a summary of the role

Data is only powerful when everyone agrees what it means. This is your chance to build a world class product behavioural data system and process from the ground up.

We're rebuilding the internal product our teams use to measure product success and end-user value across all our 24+ products in four business units.

You'll design the single source of truth: the metric definitions, the telemetry standards, the data contract engineering instruments against, the change governance processes and tooling, and the models that turn behaviour into portfolio insight.

You'll educate and support the teams using this internal behavioural data product to set expectations and and plot a rational roadmap for the maturing of this product, helping our user to use the data honestly and wisely.

Usage tells you what happened, not why, or whether it mattered. We want someone who sets quantitative behaviour against qualitative evidence and treats the disagreement as the interesting part.

AWS/Snowflake is our spine; capture and BI are open decisions you'll help make. Your models feed the business reviews our executive team, CTO and investors use to run the portfolio, and engineering is committed to instrument against your contract.

Here's what your first twelve months should look like

• First 90 days. Learn the estate, meet the engineers who instrument it, and agree the measurement model for one product end to end — with a defensible adoption number and the qualitative read on what it means.

• Three to six months. Data contract published, instrumentation spec landed with engineering, first business unit building against it. Definitions, naming and versioned change control governed.

• Six to twelve months. Rolling out across the remaining business units through the BU-aligned analysts, portfolio reporting running on your numbers.

Here's a breakdown of what you'll do (not all of it, just the important stuff)

• Design the behavioural telemetry — event and entity-state schemas at a grain that supports both portfolio roll-up and feature-level decisions — and write the instrumentation specification engineering builds against. Then assure what comes back: coverage, freshness, reconciliation.

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