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.

• Own the measurement standard — canonical definitions, naming, field ownership, versioned change control — across every business unit.

• Build the models — SQL, BI and roll-up logic turning behaviour into insight on adoption, time-to-value and value realisation.

• Triangulate. Pair behavioural data with qualitative evidence from design research and VoC, segment by user role and account, and say so publicly when the two disagree.

• Partner on the product, customer and user taxonomies, set the technical standard for the BU-aligned analysts, and help shape the analytics stack as we revamp it.

These are the essentials you'll need to get an interview

• Direct experience building mixed-method behavioural insight systems — you've built the layer where quantitative behaviour and qualitative evidence come together, not just consumed the output. You've worked alongside design or UX researchers, and can name a time the qualitative read changed what you measured.

• Hands-on event and telemetry schema design — Pendo, Amplitude, Segment, Mixpanel or equivalent. You designed the schema and wrote the spec, not used dashboards someone else built.

• Designing and governing product data models and taxonomies — canonical metrics and naming taken into a multi-product estate and made to stick, including acquired products.

• Data governance practice: schema and field ownership, versioned change control, standards adoption across teams you don't own.

• Fluency in SQL and the BI / warehouse layer — you build and validate roll-up logic yourself.

• Behavioural segmentation judgement — you know why a B2B account is a mix of people with different motivations, and why averaging them produces confident, wrong conclusions.

• The credibility to influence without authority — you can hold a standard with teams you don't line-manage.

It would be great if you had these too, but we'll support you if you don't

• Hands-on behavioural analytics and synthesis practice — running the analysis yourself, not only building what enables it.

• Privacy and lawful basis as live design constraints — pseudonymisation, residency, re-identification controls.

• GRC, governance or regulated-software domains.

• Salesforce, Jira Product Discovery, Pendo, and consolidating fragmented tooling into one system of record.

• Product value / ROI measurement — connecting behaviour to commercial outcomes.

How we'll assess this

We ask everyone to write SQL and design an event schema, and to talk us through a time qualitative evidence contradicted their numbers. No take-home longer than an hour.

Here's where this role goes next

We're building this function, not maintaining it. You set the standard the BU-aligned analysts land across the business — technical leadership from day one, without a management line.

As the team grows around you — a data scientist, a behavioural researcher, an ML engineer — the routes are principal-level ownership of product behavioural data across Diligent, or leading the team that runs it. We'd rather be straight with you than promise a title we cannot yet name. The scope is real, and it is expanding.

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