Senior Manager, Data Governance, Operations NA

Senior Manager, Operations Data Governance

This role will be based in Denver, CO. Following our flexible work policy (3 days in-office, 2 days flexible). The Senior Manager, Operations Data Governance enables North America Operations to use data confidently and consistently to manage performance, identify risk, prioritize action, and improve operational outcomes. This individual contributor establishes and operationalizes the governance practices needed to ensure structured and unstructured Operations data is clearly defined, appropriately owned, reliable, traceable, and fit for purpose.

The role works directly with Operations leaders, functions, data owners, and stewards to clarify accountability, standardize critical data and metric definitions, resolve quality issues, and embed governance into everyday business processes. It translates enterprise data standards into practical approaches that improve data trust and usability without creating unnecessary complexity for Operations.

The role serves as a connective point across Operations, Operations Data & Intelligence, Global Data & AI, and technology teams, establishing feedback loops that translate operational needs, data issues, and user feedback into improvements to data definitions, controls, products, and source systems. The ideal candidate combines strong data-governance expertise with hands-on execution, business partnership, and the ability to drive cross-functional accountability and continuous improvement.

Essential Job Functions

Operations Data Governance

• Develop and maintain the Operations data-governance framework, roadmap, decision rights, and operating practices in alignment with enterprise standards.

• Operationalize data-owner and data-steward responsibilities across Operations functions, including clear expectations, governance forums, escalation paths, and accountability.

• Facilitate governance decisions and drive resolution of cross-functional data ownership, definition, quality, access, and usage issues.

Data Quality, Observability, and Trust

• Partner with business and data teams to identify critical data elements and establish quality rules, validation criteria, thresholds, and controls.

• Monitor data-quality and trust indicators, identify root causes, and coordinate remediation with business owners, source-system owners, and data teams.

• Maintain visibility into governance risks, ownership gaps, unresolved data issues, and remediation progress.

Definitions, Metadata, and Lineage

• Establish and maintain Operations business definitions, metric definitions, ownership records, approved sources, and domain metadata.

• Promote consistent use of governed definitions across data products, semantic models, dashboards, analytics, operational reporting, and AI-enabled solutions.

• Coordinate documentation of data lineage and partner with Global Data & AI to align Operations practices with enterprise catalog, metadata, master-data, and reference-data standards.

Governance Technology and Automation

• Leverage approved AI-enabled, automation, and workflow tools to accelerate data discovery, metadata enrichment, definition development, issue triage, impact analysis, and steward support.

• Partner with data engineering and platform teams to translate governance requirements into automated quality tests, controls, alerts, workflow triggers, and deployment checkpoints.

• Evaluate emerging data- and AI-governance capabilities and recommend practical applications that improve governance effectiveness, adoption, and scalability.

Data Product, Analytics, and AI Governance

• Embed governance requirements and checkpoints into the lifecycle of data products, analytics, and AI solutions, from intake and design through deployment, change, and retirement.

• Translate governance requirements into actionable acceptance criteria, documentation, controls, and remediation plans.

• Operationalize enterprise AI-governance requirements within Operations by documenting intended use, data sources, and provenance; supporting risk assessment; and establishing appropriate quality, access, human-oversight, and monitoring controls.

• Partner with business, data, technology, risk, and compliance teams to ensure solutions meet applicable requirements for security, privacy, transparency, and responsible use.

Adoption and Continuous Improvement

• Develop practical playbooks, training, communications, and support resources for data owners, stewards, product teams, and data users.

• Establish feedback loops across Operations, Operations Data & Intelligence, Global Data & AI, and technology teams to identify recurring gaps, drive cross-functional resolution, and incorporate lessons learned into governance practices.

• Establish measures for ownership coverage, definition completeness, data quality, issue resolution, governance adoption, and maturity.

• Continuously improve governance workflows and tools to increase transparency, accountability, usability, and scalability.

• Analyze recurring data issues, user feedback, and governance metrics to…