About the role :

Cvent is scaling its product analytics capability to serve a large, multi-product portfolio (Attendee Hub, Registration/Event Management, On Arrival, Marketplace/CSN, Exhibitor Solutions, and Cvent Essentials).

We need a senior leader to build the operating system for product analytics from metric contracts and instrumentation to a governed semantic layer and self-serve insights so teams can move from question , decision in minutes, not weeks.

- Metric Contracts & Semantic Layer : Define and govern product KPIs and their lineage (adoption, activation, engagement, feature usage, time-to-value, Events Under Management (EUM), retention) and tie them directly to commercial outcomes (GRR/NRR, expansion, contraction).

- Instrumentation Engineering : Standards, naming/versioning, tracking plans, CI checks, coverage dashboards, and error budgets for data quality (freshness, accuracy, completeness).

- Self-Serve Insights & Enablement : A scalable, governed self-serve model (standard dashboards + explores), data literacy curriculum, office hours, and durable documentation.

- Identity & Data Design : User/account identity resolution across web, mobile, onsite devices (e., badge printers/kiosks), and partner integrations; deterministic keys and join strategies.

- Analytics Operating Cadence : Monthly decision readouts, portfolio-level roll ups, and What We Learned syntheses that change roadmaps and bet sizing.

- Tooling Strategy & TCO : Rationalize and integrate the analytics stack (product analytics, BI/semantic layer, observability, feature flags); drive buy-vs-build decisions and vendor governance.

- Team & Org Design : Work closely with leaders / managers who can run Platform & Instrumentation, Decision Science, and Insights & Enablement.

- Establish clear interfaces with Data Engineering, Security/Privacy, PMM, CS, and UXR.

How we'll measure success :

- Instrumentation Coverage : ?95% of GA features ship with validated tracking plans; minimal schema breakages escaping to prod.

- Reliability SLAs : Data freshness within target windows for core dashboards; accuracy/completeness within agreed error budgets.

- Self-Serve Adoption & Satisfaction : High monthly active use by PMs in governed explores/dashboards; PM CSAT , target.

- Decision Latency : Significant reduction in time from question , decision in pilot business units.

- Business Linkage : Documented cases where analytics led to changes in roadmap/investment and moved EUM, adoption, or GRR/NRR.

Key focus areas :

- Platform & Instrumentation : Tracking plans, CI, observability, coverage dashboards, data contracts.

- Decision Science : Deep dives, driver trees, account health models, right-sized experimentation playbook.

- Insights & Enablement : Standard dashboards, governed explores, literacy curriculum, office hours, documentation.

How you'll work with partners :

- Product Management : Metric definitions, priorities, evidence-backed decisions.

- Data Engineering : Pipelines, models, contracts, observability, cost; joint SLAs.

- Security/Legal/Privacy : PII handling, retention, consent, governance.

- UX Research : Pair on mixed-methods insights; Product Analytics focuses on quant, UXR on qual craft and Research Ops.

- PMM/CS/RevOps : Win/loss themes, adoption/usage insights, account health signals that tie to commercial outcomes.

What you will be doing :

- Publish the Cvent Product Metrics Charter (north stars, driver trees, metric definitions, ownership, SLA for freshness) and keep it current.

- Stand up tracking plans and CI checks tied to PRDs; reach high instrumentation coverage for critical flows across products.

- Build a governed semantic layer and standard portfolio dashboards that roll up by product, persona, and account.

- Launch a data literacy program (workshops, office hours, docs) to drive confident self-serve use by PMs, PMM, UX, CS, and leaders.

- Partner with Data Engineering on data contracts, dbt models, observability, cost management, and access controls; partner with Security/Legal on PII, retention, and privacy-by-design.

- Operationalize account-level analytics (seats/licenses, feature entitlements, health scoring, expansion/contraction funnels) with explicit links to GRR/NRR.

- Produce decision-quality narratives (not just dashboards) : monthly What we learned, portfolio scorecards, and ad-hoc deep dives for exec forums.

- Hire, coach, and retain a high-performing team; set career paths, operating rhythms, and quality bars.

What you will need for this position :

- 10 to 12+ years in product analytics/decision science for enterprise or B2B SaaS; 4+ years leading managers and building multi-disciplinary teams.

- Proven ownership of metric governance & semantic layers (e., LookML/semantic models or equivalent) across multiple products.

- Expert SQL; proficiency with Python for analysis and production-grade notebooks.

- Demonstrated success establishing instrumentation standards, CI checks, and data quality SLAs (freshness/accuracy/completeness) in partnership with Data Engineering.

- Experience unifying user/account identity across surfaces and offline/onsite data sources.

- Track record driving self-serve adoption and data literacy at scale (training, playbooks, enablement).

- Experience measuring and operationalizing GenAI/ML systems in production, including defining success metrics, evaluating offline and online performance, supporting experimentation and human-in-the-loop feedback, and translating model behavior into product and business decisions.

- Executive presence and storytelling : turning evidence into clear choices that change roadmaps and investment.

Nice-to-have :

- Exposure to experimentation at scale (A/B, holdouts, basic variance reduction) and the judgment to right-size usage.

- Experience mapping product behaviors to commercial metrics (GRR/NRR, expansion/contraction) and account health scoring.

- Familiarity with event-driven architectures, product telemetry on mobile/edge devices, and privacy-by-design.

Preferred tools & practices :

- Product analytics & telemetry (e., Mixpanel, Rudderstack, custom event pipelines), BI/semantic layer (e., Sigma), data warehouse (e., Snowflake), notebooks, observability/quality , feature flags (e., LaunchDarkly), documentation hubs, and modern CI/CD.