Description
The Senior Data Governance Manager owns Marcom’s data governance strategy and infrastructure, making sure the data powering AI tools, models, and workflows is classified, protected, accessible to the right people, and reliable enough to drive decisions. This person charts the path to a centralized data lake where Marcom’s data is managed, cataloged, and governed in one place.
Minimum Qualifications
10+ years of experience in data governance, data architecture, data management, or a related discipline, including leadership of programs at scale.Track record of building and operationalizing data governance frameworks, including data quality management, metadata management, and access control.Experience defining data classification, lineage, and access control policies across complex organizations, including role-based, attribute-based, or equivalent models for data with tiered confidentiality.Working knowledge of data architecture patterns (data lakes, warehouses, pipelines) and how they support AI and machine learning workloads.Experience designing or governing centralized data platforms with API-based integration patterns, and understanding of role-based and attribute-based access control models in environments with tiered confidentiality or disclosure requirements.
Preferred Qualifications
Demonstrated ability to influence and align senior stakeholders, with ability to explain data concepts in business terms to non-technical audiences. Experience governing data that feeds AI and machine learning systems, including training data quality, bias detection, and data provenance.Background working alongside data science team and understanding their data needs, with a focus on governance that enables their work rather than slowing it down.Familiarity with data catalog and metadata management tools, cloud data platforms, modern data stack technologies, and automated data validation approaches.Familiarity with API gateway patterns and emerging AI integration approaches (such as MCP).Experience in marketing, creative, or media environments, including an understanding of campaign data, creative assets, and audience data, where data sensitivity and pre-announcement confidentiality are critical.Knowledge of data quality frameworks and automated data validation approaches.
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