Data Analyst & Steward

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Job Description

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.