AI & Data Consultant – Data Governance, Authorized Provisioning Point

Responsibilities

• Support the enterprise Authorized Provisioning Point / Authorized Data Source program within the AI & Data Organization
• Support the definition of a data domain based model, partner with stakeholders across architecture, technology delivery, and the LOB to define strategies and implementation roadmaps to bring the program into reality
• Support program execution and asset certification
• Draft and maintain corresponding policies, frameworks, standards, and procedures
• Support data-products strategy and intersection with enterprise ontologies
• Support the definition and implementation of an operating model to cover data asset ownership and data stewardship
• Support the implementation of core data management and governance capabilities against identified data assets
• Develop and maintain policies, standards and technology in support of enterprise Data Governance
• Create and implement processes and procedures to support data standards and control frameworks
• Provide subject matter expertise in the planning and execution of data management activities such as data sourcing, metadata management, data quality, data privacy or records management
• Drive solution design and execution of multi-pronged data governance efforts that span across platforms and business/technology areas to address enterprise data needs
• Implement strategic data governance capabilities by working with business and technology partners to document requirements and create action plans
• Perform oversight and inspection of critical data management practices for adherence to established data policies and control frameworks
• Identify data issues and work with technology, risk and business partners to triage, track, communicate, mitigate and remediate the issues to closure
• Support the production of metrics and measures to track progress and adherence, escalating as needed to various committees for visibility and program accountability
• Understand and integrate relevant regulations and industry trends into work plans, exploring emerging frameworks and assessing feasibility using proof of concepts
• Collaborate closely with peer teams to ensure coordination, synergy and seamless overall execution
• Champion and drive improvement in enterprise data management practices throughout the organization

• Bachelor’s degree in Computer Science, Data Science, AI, Software Engineering, or related field
• Minimum of 5 years of professional experience in AI and data
• Knowledge of AI models, data architectures, and analytics methodologies
• Preferred Qualifications:
  
  Experience with enterprise data governance platforms such as Collibra, Informatica Axon, Alation, Solidatus, or equivalent tools
• Demonstrated ability to define and implement data governance operating models, including stewardship, ownership, and accountability structures
• Working knowledge of cloud data ecosystems (e.g., AWS, Azure, Google Cloud), including data governance, data cataloging, and data security capabilities
• Experience developing metadata strategies, lineage visualization, and automated metadata harvesting
• Familiarity with regulatory frameworks impacting data (e.g., GDPR, CCPA, BCBS 239, HIPAA, SOX) and experience operationalizing compliance requirements
• Proven ability to execute cross-functional data remediation or data quality improvement initiatives with measurable outcomes
• Strong analytical skills with experience building dashboards, KPIs, and data quality scoring using tools such as Power BI, Tableau, or similar platforms
• Experience with Agile delivery methodologies and working within Agile teams or supporting Agile data governance enablement
• Advanced communication, influencing, and facilitation skills, particularly in communicating complex data concepts to non-technical audiences
• Proven ability to guide organizations through data culture maturity improvements, including training and change management operations
• Experience evaluating and integrating AI/ML data governance considerations (e.g., model data lineage, training data controls, bias auditing)