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, we’re transforming the meetings and events industry through innovative technology that powers the human connection.Director, Product AnalyticsReports to: Head of Analytics, Matrixed to Chief Product OfficerWhy this role existsCvent is scaling its product analytics capability to serve a large, multi‑product 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 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.What you will own- 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.g., badge printers/kiosks), and partner integrations; deterministic keys and join strategies. - Analytics Operating Cadence: Monthly decision readouts, portfolio‑level 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 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.Note on experiments: While experimentation isn’t the primary focus today, you will establish right‑sized guardrails and a playbook (e.g., A/B where feasible, holdouts, basic power/MDE guidance, SRM detection) so the org is future‑ready without over‑rotating now.What you’ll 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 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.QualificationsMust‑have- 10–12+ 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.g., 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.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.Preferred tools & practicesProduct 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.

Apply Now