Lead, Data Governance

Overview: 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 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. As a Data Governance POC with AI expertise, you will play a critical role in managing and optimizing our organization's data assets, ensuring they are accurate, reliable, and accessible. Your responsibilities will include understanding Enterprise data flow and integrations, developing AI-driven strategies for data quality improvement, and supporting data governance initiatives. You will work closely with cross-functional teams to ensure seamless data operations and drive innovation in data management practices. In This Role, You Will: Data Governance & Standardization Establish and maintain the enterprise data glossary, dictionary, and domain taxonomy , ensuring consistent definitions and understanding across systems and business units. Partner with data domain owners, stewards, and architects to define key business entities and data elements , including lineage, usage, and ownership. Work with the Enterprise Data Council (EDC)  and domain SteerCos to ensure alignment between governance policies and operational needs. Data Enablement & AI Readiness Enable structured and unstructured data  for use by downstream systems such as LLMs, AI agents, and orchestration platforms  (e.g., IBM Watson, Superblocks, Mercato, Clark). Collaborate with IT, data engineering, and analytics teams to make metadata discoverable and consumable for automation and AI workloads. Identify and prioritize data quality gaps that impede AI/ML use cases; propose and drive remediation plans. Operational Execution Partner with business users to translate requirements into data definitions, ensuring traceability from source to consumption. Document and standardize data flows, lineage, and ownership  within the Snowflake environment and connected ecosystems. Support ongoing STAR program objectives by embedding governance and metadata quality into project delivery milestones. Stakeholder Collaboration Work closely with executives, product, IT, and analytics teams to ensure that data definitions support both business reporting and AI transformation goals . Communicate progress and challenges clearly to leadership, drive alignment through influence and domain expertise. Here's What You Need: Bachelor’s degree in Information Management, Computer Science, Data Science, or related field (Master’s preferred). 5-10 years of experience in data governance, metadata management, or data architecture  roles. Hands-on experience with Snowflake, Salesforce, Oracle, or similar enterprise data platforms . Familiarity with AI/ML data pipelines  or metadata requirements for LLMs is a plus. Strong understanding of data quality, lineage, cataloging, and governance frameworks (e.g., DAMA-DMBOK). Excellent collaboration and communication skills, with ability to bridge technical and business conversations. Proven track record of delivering incremental, measurable improvements in data usability  and governance maturity. Data Governance & Standardization Establish and maintain the enterprise data glossary, dictionary, and domain taxonomy , ensuring consistent definitions and understanding across systems and business units. Partner with data domain owners, stewards, and architects to define key business entities and data elements , including lineage, usage, and ownership. Work with the Enterprise Data Council (EDC)  and domain SteerCos to ensure alignment between governance policies and operational needs. Data Enablement & AI Readiness Enable structured and unstructured data  for use by downstream systems such as LLMs, AI agents, and orchestration platforms  (e.g., IBM Watson, Superblocks, Mercato, Clark). Collaborate with IT, data engineering, and analytics teams to make metadata discoverable and consumable for automation and AI workloads. Identify and prioritize data quality gaps that impede AI/ML use cases; propose and drive remediation plans. Operational Execution Partner with business users to translate requirements into data definitions, ensuring traceability from source to consumption. Document and standardize data flows, lineage, and ownership  within the Snowflake environment and connected ecosystems. Support ongoing STAR program objectives by embedding governance and metadata quality into project delivery milestones. Stakeholder Collaboration Work closely with executives, product, IT, and analytics teams to ensure that data definitions support both business reporting and AI transformation goals . Communicate progress and challenges clearly to leadership, drive alignment through influence and domain expertise. Bachelor’s degree in Information Management, Computer Science, Data Science, or related field (Master’s preferred). 5-10 years of experience in data governance, metadata management, or data architecture  roles. Hands-on experience with Snowflake, Salesforce, Oracle, or similar enterprise data platforms . Familiarity with AI/ML data pipelines  or metadata requirements for LLMs is a plus. Strong understanding of data quality, lineage, cataloging, and governance frameworks (e.g., DAMA-DMBOK). Excellent collaboration and communication skills, with ability to bridge technical and business conversations. Proven track record of delivering incremental, measurable improvements in data usability  and governance maturity.