Data Quality Product Owner (Data Governance)

TL;DR

Data Quality Product Owner (Data Governance): Defining and driving the product vision for enterprise-wide data quality and governance initiatives with an accent on data reliability, accuracy, and scalability. Focus on designing automated data quality rules, establishing KPIs for data trust, and leading cross-functional Agile delivery.

Location: Must be based in the United States

Company

A data-driven enterprise environment focusing on high-impact data governance and reliability.

What you will do

• Own and manage the data quality and governance product roadmap and value-driven backlog.
• Implement enterprise data quality frameworks, including standards, business rules, and remediation workflows.
• Establish and track KPIs to ensure improvements in accuracy, completeness, and reliability across enterprise datasets.
• Collaborate with engineers and stakeholders to translate requirements into actionable user stories.
• Lead Agile delivery processes using Jira and Confluence for sprint planning and release coordination.
• Design and optimize automated data quality rules and monitoring capabilities across enterprise platforms.

Requirements

• 7+ years of experience in Data Management, Data Governance, or Product Ownership roles.
• Proven experience leading enterprise-level data quality or data governance initiatives.
• Hands-on experience with SQL, Azure, Databricks, Monte Carlo, or Informatica IICS.
• Strong experience working in Agile/Scrum environments using Jira and Confluence.
• Must be based in the United States.
• Excellent analytical, problem-solving, and stakeholder management skills.

Nice to have

• Familiarity with Master Data Management (MDM) and data lineage.
• Product certifications such as CSPO or SAFe Product Owner/Product Manager.

Culture & Benefits

• Fully remote work arrangement within the US.
• Opportunity to lead high-impact, enterprise-scale strategic initiatives.
• Collaborative Agile environment with cross-functional exposure.
• Exposure to modern data platforms and tools including Azure and Databricks.
• Competitive compensation aligned with experience and market benchmarks.