Job Description & How to Apply Below
- 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.
The DNA of Cvent is our people, and our culture has an emphasis on fostering intrapreneurship – a system that encourages Cventers to think and act like individual entrepreneurs and empowers them to take action, embrace risk, and make decisions as if they had founded the company themselves. At Cvent, we value the diverse perspectives that each individual brings. Whether working with a team of colleagues or with clients, we ensure that we foster a culture that celebrates differences and builds on shared connections.
About the role:
Cvent 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.
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.
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.
Securi…
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