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Lead Data Governance Analyst

Job in Tempe, Maricopa County, Arizona, 85285, USA
Listing for: Circle K Stores Inc.
Full Time position
Listed on 2026-07-24
Job specializations:
  • IT/Tech
    Information Security & Data Protection, Information & Knowledge Management, Data Warehousing, Data Engineering
Salary/Wage Range or Industry Benchmark: 140000 - 190000 USD Yearly USD 140000.00 190000.00 YEAR
Job Description & How to Apply Below

About Circle K At Circle K, data is a strategic enterprise asset that powers growth, operational excellence, customer engagement, analytics, and AI innovation. As part of our Enterprise Data Governance organization, we are building modern governance capabilities that enable trusted data, certified data products, AI readiness, and scalable business value across a global enterprise.

Position Summary

The Senior Data Governance & AI Enablement Lead is responsible for advancing Circle K's enterprise data governance strategy with a focus on:
Policy Implementation, Stewardship Coordination, AI Governance, and Enablement, Data Product Governance, Data Platform Certification, Metadata and Catalog Oversight, Data Quality and Trust, Data Monetization Enablement, Compliance Support, Governance Adoption and Change Management. This role will partner closely with Data Product Managers, Business Product Managers, Data Stewards, Data Engineering, Analytics, Architecture, Security, Privacy, AI, and Business stakeholders to establish trusted, discoverable, reusable, and certified data assets that drive business outcomes and support responsible AI adoption.

A key responsibility will be leading governance and certification efforts across Snowflake Horizon Catalog, Databricks Unity Catalog, and Informatica, ensuring business users and AI solutions have access to trusted and governed data products. Success in this role will be measured through increased adoption of certified data products, improved data trust, accelerated data access, enhanced AI readiness, and measurable business value generated through governed data assets.

Key Responsibilities

AI Governance & Enablement Support the development and implementation of enterprise AI Data Governance frameworks, standards, and controls. Partner with AI, Analytics, and Data Science teams to ensure AI solutions utilize trusted and governed data assets. Support AI governance reviews, risk assessments, and approval processes. Establish metadata, lineage, and data quality requirements that improve AI readiness. Drive responsible AI practices aligned with regulatory, privacy, and governance requirements.

Data Products & Data Monetization Support the development, certification, and governance of enterprise Data Products across Customer, Item, Site, Vendor, Finance, Loyalty, Fuel, HR, and Digital domains. Partner with Data Product Managers and business stakeholders to define governance requirements and success criteria for data products. Enable Data Monetization opportunities by ensuring data assets are discoverable, trusted, reusable, and scalable. Support measurement of Data Product adoption, usage, business value, and operational effectiveness.

Identify opportunities to leverage data as a strategic asset to improve revenue, reduce costs, enhance customer experiences, and support AI innovation. Develop governance processes that accelerate self‑service analytics and data product consumption while maintaining appropriate controls.

Data Platform Certification Establish and manage enterprise certification processes for datasets and data products. Drive adoption of trusted and certified assets across Snowflake and Databricks. Define certification standards including:
Business ownership, Data stewardship, Metadata completeness, Data quality thresholds, Lineage validation, Regulatory compliance, Security classification. Maintain certification workflows, scorecards, and reporting.

Metadata & Catalog Governance Manage business and technical metadata standards across Informatica, Snowflake Horizon Catalog, and Databricks Unity Catalog. Support implementation of the Enterprise “Catalog of Catalogs” strategy. Drive business glossary development and stewardship. Improve data discoverability through metadata enrichment and governance automation. Support synchronization of metadata, classifications, glossary terms, lineage, and governance information across platforms.

Data Quality & Governance Operations Partner with business and technical teams to identify Critical Data Elements (CDEs). Support enterprise data quality monitoring, scorecards, issue management, and remediation processes.…

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