AI HUB Data Steward

Role summary:

Ensures that Quality data is trusted, understood, governed, and fit for business and AI usage. This role acts as the bridge between business stakeholders and technical teams by defining data meaning, monitoring data quality, maintaining metadata, and enforcing governance standards. The Data Steward ensures that AI, analytics, and operational decisions are based on reliable and well-understood data assets.

Role at a glance:

• Main focus: Data quality, business data definitions, metadata management, governance, lineage, and data adoption.

• Typical outputs: Business glossaries, data dictionaries, data quality dashboards, quality rules, governance documentation, metadata repositories, lineage documentation, and data issue remediation plans.

• Key interfaces: Quality business teams, AI Product Owners, Data Engineers, Data Scientists, Foundry experts, IT, cybersecurity, data governance teams, and data owners.

Key responsibilities:

· Define and maintain business data definitions, metadata, data dictionaries, and Quality domain business glossaries.

· Act as the subject matter expert for Quality data domains, ensuring that data meaning, ownership, and usage are clearly documented and understood.

· Establish, monitor, and continuously improve data quality rules, controls, and performance indicators across Quality data assets.

· Identify, investigate, and coordinate resolution of data quality issues with business and technical stakeholders.

· Ensure compliance with enterprise data governance standards, access policies, security requirements, and regulatory obligations.

· Maintain visibility of data lineage, transformations, and dependencies to improve transparency and trust in AI and analytics solutions.

· Support Data Engineers and Data Scientists by validating business meaning, source data interpretation, and quality expectations.

· Measure data adoption, user satisfaction, and business usage patterns to help prioritize improvements and maximize business value.

· Promote data literacy and governance awareness across Quality teams through coaching, documentation, and stakeholder engagement.

· Participate in AI and data initiatives to ensure that trusted, governed, and business-relevant data is available throughout the delivery lifecycle.