Senior Data Governance Manager (Marketing Communications)

Quick Facts

• Role: Senior Data Governance Manager (Marcom)

• Focus: Data governance strategy and centralized data platform for AI-enabled marketing communications

Description

Own Marcom’s data governance strategy and infrastructure to ensure AI tools, models, and workflows rely on data that is classified, protected, accessible to the right people, and reliable for decision-making. Chart the path to a centralized data lake where Marcom’s data is managed, cataloged, and governed in one place.

Responsibilities

• Define and own Marcom’s data governance strategy and framework (data quality, classification, lineage, ownership, retention, access control)

• Architect and drive implementation of a centralized data lake with operational tool integration via APIs and MCPs (avoiding siloed copies)

• Establish data quality standards, validation processes, and monitoring; build data lineage and provenance tracking

• Define and enforce data classification and sensitivity policies with AI Governance and security partners; implement disclosure-based access governance

• Create and maintain a data catalog and metadata management practice; establish data ownership and stewardship models

• Define data readiness criteria for AI initiatives so data is governed before training, fine-tuning, or inference

• Set integration governance for tools connecting to the data lake (authentication, data freshness, write-back validation, audit logging)

• Serve as primary governance liaison with Legal, Privacy, Security, IS&T, Marcom Analytics, Procurement, and adjacent Marketing organizations

Requirements

• 10+ years of experience in data governance, data architecture, data management, or related discipline, including leadership of programs at scale

• Track record building and operationalizing data governance frameworks (data quality, metadata management, access control)

• Experience defining data classification, lineage, and access control policies across complex organizations, including role-based or attribute-based models for tiered confidentiality

• Working knowledge of data architecture patterns (data lakes, warehouses, pipelines) supporting AI/ML workloads

• Experience designing or governing centralized data platforms with API-based integration patterns; understanding RBAC/ABAC models in tiered confidentiality or disclosure environments

Benefits

Benefits were not specified in the provided posting.