Lead, Data Governance

Royal Caribbean Cruises Ltd•Miami, FL

3d•Onsite

About The Position

Journey with us! Combine your career goals and sense of adventure by joining our exciting team of employees. Royal Caribbean Group is pleased to offer a competitive compensation and benefits package, and excellent career development opportunities, each offering unique ways to explore the world. We are proud to be the vacation-industry leader with global brands — including Royal Caribbean International, Celebrity Cruises and Silversea Cruises — the most innovative fleet and private destinations, and the best people. Together, we are dedicated to turning the vacation of a lifetime into a lifetime of vacations for our guests. The Royal Caribbean Group’s Data Governance Team has an exciting career opportunity for a full-time Data Governance Lead reporting to the Data Governance Director. This position will work on-site in Miramar, Florida and commute between Miramar and Miami office locations as required. This position is also not eligible for work authorization sponsorship. Position Summary: The Data Governance Lead is responsible for supporting the organization’s data governance, data catalog, metadata management, and responsible AI governance capabilities. This role collaborates with business data stewards, technology teams, and governance stakeholders to improve the reliability, usability, and trustworthiness of enterprise data and AI assets. The position supports data stewardship, data catalog, AI governance, policy implementation, issue remediation, and governance reporting activities across the enterprise. This role supports governance initiatives, facilitates cross-functional working sessions, drives adoption of standards and best practices, and contributes to reporting on governance maturity, data catalog, and compliance. The role also supports responsible enterprise AI adoption by establishing AI governance metadata controls, lifecycle documentation, monitoring practices, and audit-ready evidence for generative AI, machine learning, and agentic AI solutions.

Requirements

BS Degree in Mathematics, Economics, Computer Science, or Information Management.
6+ years of progressive experience in data governance, data management, data quality, reporting, analytics, or a related enterprise data role.
3+ years supporting the governance, stewardship, quality, delivery, or lifecycle management of enterprise data products, data domains, or critical data assets.
Excellent knowledge of SQL.
Qualified with Azure Cloud and Databricks technologies.
Experience in a modern programming language, such as Python.
Experience supporting AI governance, responsible AI, or technology programs; familiarity with frameworks such as NIST AI RMF, ISO/IEC 42001, EU AI Act concepts, and privacy/security-by-design practices preferred.
Hands-on or working knowledge of enterprise AI governance and data governance platforms such as Microsoft Purview, Collibra, OneTrust AI Governance, Databricks Unity Catalog, or similar tools.

Nice To Haves

Working knowledge of the Data Management Association (DAMA) framework, including the ability to apply its data management principles to support enterprise data governance, stewardship, data catalog, metadata, data quality, and lineage initiatives.
Experience with Data Governance concepts including master and metadata management.
Understanding of data technologies, cloud data platforms, metadata management, data lineage, data quality tooling, and their application across enterprise data domains.
Expert-level data background and knowledge of software development methodologies.
Expertise with GDPR & CCPA/CPRA and other country and state regulations.
Strong communication skills, including the ability to prepare clear recommendations, dashboards, presentations, decision documents, and risk narratives for technical and non-technical audiences.
Ability to operate independently, manage competing priorities, and drive outcomes in a complex and fast-paced environment.
Strong analytical, problem-solving, facilitation, and critical-thinking skills with the ability to synthesize complex information and support practical governance decisions.
Flexibility to operate within multiple time zones.

Responsibilities

Collaborate with business data stewards to gather, understand, and document functional requirements, data definitions, business rules, and governance expectations.
Evaluate data against enterprise data quality standards, interpret risks and trends, and translate insights into actionable recommendations for stakeholders.
Create, document, maintain, and promote data governance standards, policies, and solutions that support stewardship accountability, metadata consistency, and data quality improvement.
Monitor implemented data governance solutions for continued adherence to standards, processes, controls, and stewardship operating routines.
Participate in governance workgroups and subcommittees to establish decision rights, workflows, approvals, escalations, and repeatable best practices.
Develop partnerships with business data stewards to promote governance best practices, resolve data issues, and improve trusted usage of approved metadata.
Champion the use of approved metadata through training, knowledge-sharing sessions, stewardship enablement, and stakeholder engagement.
Leverage technologies such as SQL, Python, Power BI or Tableau, Azure, Databricks, workflow management tools, issue-tracking platforms, and enterprise data governance platforms to analyze data, document requirements, support stewardship workflows, and track governance actions.
Own and maintain enterprise data catalog content, including business glossary terms, data domains, data products, critical data elements, data owners, data stewards, classifications, data lineage, and metadata standards.
Partner with business and technical stakeholders to capture, validate, and maintain trusted metadata that improves data discoverability, understanding, reuse, and governance accountability across enterprise data domains.
Define and operationalize catalog governance standards, including required metadata fields, naming conventions, certification criteria, approval workflows, stewardship responsibilities, and ongoing metadata quality expectations.
Collaborate with data engineering, analytics, privacy, security, and architecture teams to ensure catalog entries accurately reflect source systems, data flows, lineage, usage context, sensitivity classifications, and compliance requirements.
Promote catalog adoption by supporting training, steward enablement, metadata completeness tracking, glossary usage, and continuous improvement of catalog workflows and governed data asset documentation.
Use data catalog capabilities to support responsible AI and analytics by ensuring approved data assets are searchable, trusted, properly classified, traceable, and aligned with enterprise governance standards.
Use data catalog and metadata management technologies such as Microsoft Purview, Collibra, Databricks Unity Catalog, or similar platforms to manage metadata, lineage, glossary terms, classifications, certifications, and governed data assets.
Identify and document data quality requirements and rules.
Create, enhance, and ensure adherence to data quality standards.
Develop key performance indicators to monitor and track data integrity.
Work with technical and non-technical stakeholders across the organization to measure the quality of our datasets across multiple data domains and help monitor compliance.
Troubleshoot the source of data quality issues and provide a path to remediation.
Identify, define, and contribute to data quality process improvement efforts.
Design and execute data remediation measures and implement solutions for data accuracy and reconciliation.
Use data quality and observability technologies such as Ataccama, Collibra Data Quality & Observability, Microsoft Purview Data Quality, Databricks, Azure Data Factory, and BI dashboards to define rules, profile data, monitor anomalies, track KPIs, and manage remediation.
Support the implementation of AI governance frameworks, policies, standards, operating procedures, and oversight routines aligned to responsible AI principles, enterprise risk management, privacy, security, and regulatory expectations.
Partner with Data Science, Legal, Privacy, Cybersecurity, Compliance, Product, and business teams to identify risks related to the use of metadata in AI and GenAI solutions.
Support oversight of AI governance platforms, metadata catalogs, and policy enforcement workflows that provide visibility, control, and traceability across enterprise AI initiatives.
Apply AI governance and model oversight technologies such as Microsoft Purview AI Hub, OneTrust AI Governance, Collibra AI Command Center, Databricks Unity Catalog, and monitoring platforms to support AI inventories, assessments, approvals, lifecycle evidence, and audit readiness.