Director, Product Analytics

Cvent is a leading meetings, events, and hospitality technology provider with more than 4,800 employees and ~22,000 customers worldwide, including 53% of the Fortune 500. Founded in 1999, Cvent delivers a comprehensive event marketing and management platform for marketers and event professionals and offers software solutions to hotels, special event venues, and destinations to help them grow their group/MICE and corporate travel business. Our technology brings millions of people together at events around the world. In short, were transforming the meetings and events industry through innovative technology that powers the human connection.

Director, Product Analytics

Reports to: Head of Analytics, Matrixed to Chief Product Officer

Why this role exists

Cvent is scaling its product analytics capability to serve a large, multiproduct portfolio (Attendee Hub, Registration/Event Management, OnArrival, Marketplace/CSN, Exhibitor Solutions, and Cvent Essentials). We need a senior leader to build the operating system for product analyticsfrom metric contracts and instrumentation to a governed semantic layer and selfserve insightsso teams can move from question decision in minutes, not weeks.

What you will own

• Metric Contracts & Semantic Layer: Define and govern product KPIs and their lineage (adoption, activation, engagement, feature usage, timetovalue, 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).
• SelfServe Insights & Enablement: A scalable, governed selfserve 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.g., badge printers/kiosks), and partner integrations; deterministic keys and join strategies.
• Analytics Operating Cadence: Monthly decision readouts, portfoliolevel rollups, 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 buyvsbuild 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.

Note on experiments: While experimentation isnt the primary focus today, you will establish rightsized guardrails and a playbook (e.g., A/B where feasible, holdouts, basic power/MDE guidance, SRM detection) so the org is futureready without overrotating now.

What youll do

• 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 selfserve 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 privacybydesign.
• Operationalize accountlevel analytics (seats/licenses, feature entitlements, health scoring, expansion/contraction funnels) with explicit links to GRR/NRR.
• Produce decisionquality narratives (not just dashboards): monthly What we learned, portfolio scorecards, and adhoc deep dives for exec forums.
• Hire, coach, and retain a highperforming team; set career paths, operating rhythms, and quality bars.

Qualifications

Musthave

• 15+ years in product analytics/decision science for enterprise or B2B SaaS; 4+ years leading managers and building multidisciplinary teams.
• Proven ownership of metric governance & semantic layers (e.g., LookML/semantic models or equivalent) across multiple products.
• Expert SQL; proficiency with Python for analysis and productiongrade 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 selfserve 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.

Nicetohave

• Exposure to experimentation at scale (A/B, holdouts, basic variance reduction) and the judgment to rightsize usage.
• Experience mapping product behaviors to commercial metrics (GRR/NRR, expansion/contraction) and account health scoring.
• Familiarity with eventdriven architectures, product telemetry on mobile/edge devices, and privacybydesign.

How well 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.
• SelfServe 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, rightsized experimentation playbook.
• Insights & Enablement: Standard dashboards, governed explores, literacy curriculum, office hours, documentation.

How youll work with partners

• Product Management: Metric definitions, priorities, evidencebacked decisions.
• Data Engineering: Pipelines, models, contracts, observability, cost; joint SLAs.
• Security/Legal/Privacy: PII handling, retention, consent, governance.
• UX Research: Pair on mixedmethods 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.

Preferred tools & practices

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