Data Analyst & Steward

Scope and Impact You are an autonomous contributor holding end-to-end stewardship accountability for an assigned scope, on behalf of the business owners of that data. You balance stewardship responsibilities, analytics delivery, and self-service enablement while maximizing business value from data through trusted metrics, high-quality data products, and adoption of analytical solutions. You may sit within a regional IT organization or directly within the business domain, depending on structure.

As a domain resource, you receive governance standards, tooling, and escalation support from the central Data & AI Governance function, Global Data & AI Platform team and the visualization engineering CoE. What you do Metrics & Data Products Align existing and new metric definitions across teams so everyone works from the same numbers. Curate metric definitions, metadata, and the semantic layer so they're reliable enough for AI agents to answer business questions directly, not just for human dashboard users.

Act as Data Steward for all data products within your assigned sub-domain. Monitor and improve data quality through controls and remediation activities, elevating data quality and data management from source to the analytical layer. Maintain metadata, data lineage, business glossaries, and ownership information within the enterprise data catalog.

Track and demonstrate the business value and ROI realized from the data products you steward, using this evidence to prioritize improvements or retirement. Partner with Data Engineers to define the semantic layer in the global data and AI platform and understand how they built data models and pipelines. Review and coordinate data access approvals and stewardship workflows in accordance with governance policies and business ownership responsibilities.

Analytical Solutions Support business stakeholders with complex, non-standard analytical questions that go beyond self-service, applying analytical rigor to validate findings before they inform decisions. Drive adoption of self-service analytics developed with data engineers. Build and maintain complex dashboards and analytical solutions.

Validate that self-service and analytical outputs remain accurate when consumed by AI agents, not only by human users. Data Literacy Take ownership for growing data literacy within your sub-domain. Train and support business users on self-service analytics solutions AI-readiness & Ways of Working Proactively automates routine parts of their own workflow (quality checks, documentation updates, recurring reporting) using agentic AI, and prototypes solutions live with business stakeholders using AI instead of running a traditional multi-week build cycle.