Product Owner

Denver
|2 years of exp
|Contract

Posted: 1 week ago• Recruiter recently active

Job Location

Denver

Remote Work Policy

In office - WFH flexibility

Visa Sponsorship

Not Available

RelocationAllowed

Skills

SQL

Jira

Github

Snowflake

Confluence

About the job

We’re looking for a detail-oriented Data Product Owner to help translate business needs into clear, actionable work and support the delivery of internal data products, governed metrics, and AI-enabled analytics.

Position Details

Start Date: ASAP
Type: Direct Hire
Location: Denver, CO
Schedule: Hybrid, 2 days on-site
Compensation: Starting at $80K + benefits

About the Role

Reporting to the Product Manager for Enterprise Data, this role will bridge business and technical teams by helping define requirements, manage priorities, coordinate delivery, and keep stakeholders aligned throughout the development process.

Key Responsibilities

Manage and prioritize the backlog for data products and stakeholder requests.
Translate business needs into requirements, user stories, acceptance criteria, and actionable work items.
Coordinate with data engineering, BI, finance, operations, IT, and business stakeholders.
Support intake, backlog refinement, UAT, release readiness, and stakeholder follow-up.
Maintain documentation for requirements, data definitions, metrics, and business rules.
Help improve Agile processes, Jira organization, delivery visibility, and release coordination.

What We're Looking For

2+ years of experience in product ownership, business analysis, product operations, data operations, project coordination, or a similar role.
Experience translating business needs into structured requirements and work items.
Experience with UAT, backlog refinement, release management, documentation, and Agile processes.
Experience with tools such as Jira, Confluence, GitHub, or Snowflake.
Strong organization, communication, follow-through, and stakeholder coordination skills.
Comfortable working with data, SQL, analytics platforms, and both technical and non-technical teams.
Familiarity with data quality, metric definitions, data dictionaries, or governed analytics.

Nice to Have: Experience with data products, self-service analytics, semantic layers, dbt, Streamlit, or similar technologies.