Marketing Data, Artificial Intelligence

Cvent is a rapidly growing company offering industry-leading software solutions to over 300,000 event professionals and hoteliers worldwide. For over twenty years, Cvent has been at the forefront of transforming the meetings and events industry, contributing over $1.5 trillion to the global GDP and impacting nearly 26 million jobs. Our culture of innovation and collaboration makes Cvent an exciting place to grow your career.

Role Overview

Cvent’s Global Demand Center is seeking an AI Data Lead, Marketing Systems to build and operationalize marketing’s agent-ready, governed data foundation.

In this hands-on role, you will connect our CDP, data warehouses, revenue systems, and AI ecosystems to deliver integrated, contextual data and insights for marketers and sellers. You will be responsible for data modeling, pipelines, identity resolution, feature stores, and the governed data products that drive AI agents and analytics across marketing. Your key responsibilities will include:

- Delivering clean, trusted, and reusable data products for AI and analytics.
- Accelerating approved AI use cases with scalable, self-service data.
- Embedding security, privacy, and compliance by design.
- Achieving 30-50% faster onboarding of new AI capabilities and data domains.

What You Will Do

1. Build the Marketing AI Data Foundation

- Design and maintain marketing data models and pipelines across sources such as MAP, CRM, CDP, web analytics, events, and content platforms.
- Implement and evolve schemas and dimensional models that are intuitive for both humans and AI agents to consume.
- Stand up and maintain feature stores and reusable data assets to support AI agents, predictive models, and advanced analytics.
- Partner with IT and Data Management to align on standards, patterns, and shared infrastructure.

2. Own the Identity Resolution and Context Layer

- Design and maintain an AI-ready identity resolution layer that unifies contacts, accounts, users, and events across systems.
- Define and automate matching, deduplication, and survivorship rules that balance accuracy and performance.
- Ensure that downstream AI agents, scoring models, and dashboards can reliably answer “who is this?” and “what is their context?” for marketing and sales.

3. Curate Governed Data Products and Metrics

- Define, prioritize, and deliver governed data products (tables, views, data marts, semantic layers) that support high priority AI and analytics use cases.
- Establish standard, well documented metrics and definitions for marketing.
- Package data products in a way that is discoverable, documented, and self-serve for analysts, marketers, and AI teams.
- Partner with Marketing Systems leadership and AI Strategist to align data roadmap to the AI and marketing portfolio.

4. Data Quality, Lineage, and Compliance by Design

- Implement data quality frameworks across critical datasets.
- Maintain end-to-end lineage for key data products so teams can answer “where did this data come from?” and “who owns it?”.
- Embed security, privacy, and compliance requirements into data models and pipelines (e.g., PII handling, consent flags, regional restrictions, access controls).
- Work with IT/Security and Compliance to ensure AI use is safe, governed, and auditable.

5. Dashboards, Telemetry, and Value Realization

- Automate dashboards and reporting that expose the health and performance of the AI data layer (data freshness, coverage, quality, usage).
- Build AI value realization telemetry: track how data products and AI agents are being used, where they drive time/cost savings, and where they need improvement.
- Partner with analytics teams to operationalize standardized views and Q
-ready insights, leveraging BI tools and AI-assisted analytics where appropriate.

6. Cross-Functional Collaboration and Ways of Working

- Partner with marketing teams and IT to translate business and AI requirements into clear data requirements, specs, and roadmaps.
- Act as the primary data counterpart to the Marketing AI Strategist and AI/ML engineering teams, ensuring that new agent and model work is built on governed data.
- Contribute to and help run intake, prioritization, and backlog…