Data Product Owner

ABOUT GREYSTAR

Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $300 billion of real estate in over 260 markets globally with offices throughout North America, Europe, South America, and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States, managing more than one million units/beds globally. Across its platforms, Greystar has over $79 billion of assets under management, including approximately $36 billion of development assets and over $30 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more, visit .

JOB DESCRIPTION SUMMARY

The Data Product Owner (DPO) at Greystar will play a critical role in driving build out, adoption and governance of our enterprise data platform. This DPO partners with analytics teams, data engineers, and data governance to ensure that data products are aligned, standardized, and usable across the organization. The DPO will act as the primary point of contact for Line of Business (LOB) analytics teams, define roadmaps and priorities, translate business requirements into actionable data product features and enable self-service analytics by ensuring business teams can use curated data assets.

JOB DESCRIPTION

Key Responsibilities:

Stakeholder Alignment & Road mapping

• Serve as the primary point of contact for the LOB analytics teams you support.
• Define and manage the roadmap and priorities for data initiatives in partnership with analytics leads.
• Provide updates on backlog progress, risks, dependencies, and timelines to stakeholders.

Backlog Ownership & Delivery

• Own and prioritize the Data Management Platform (DMP) backlog for the data domains they own to maximize business value and minimize downstream ripple effects.
• Gather, clarify, and document requirements (functional, technical, and data quality) and translate them into user stories and acceptance criteria .
• Drive Sprint Planning and backlog execution in partnership with engineering teams.

Prototyping & Self-Service Enablement

• Write SQL and Python to support just-in-time analysis and prototyping for the analytics teams
• Demonstrate how curated (“gold”) datasets can be leveraged to drive business outcomes by developing light weight dashboards /prototypes which can answer specific business questions, enabling analytics teams to productionalize their use cases.
• Champion self-service analytics enablement by answering questions and providing training to business teams so they can use governed data assets.

Business Rule Definition & Documentation

• Partner with domain experts and governance teams to define business rules for data transformation from bronze → silver → gold layers.
• Create source-to-target mapping documents in plain language, capturing proposed business rules and refining them based on stakeholder feedback.
• Break down business rules into detailed user stories for engineering teams to implement.

Cross-Functional Collaboration

• Align with data governance, data stewards, and architects to standardize data definitions, quality rules, and compliance controls.
• Perform testing and validation alongside engineering and QA to ensure acceptance criteria is met and data deployed to production is of high quality and able to drive business value
• Collaborate with engineering teams to industrialize data pipelines and integrate governance-driven quality controls .

Governance & Alignment

• Ensure consistency of business rules and data definitions across multiple LOBs; coordinate with Governance to resolve misalignments .
• Surface and escalate conflicting business requirements to governance teams, helping drive consensus.
• Support governance discussions by providing insights on current implementation using domain knowledge, documentation, or reverse-engineering with engineering teams when necessary.

Qualifications

• Technical Expertise in SQL and Python (able to query, analyze, and prototype solutions).
• Strong knowledge of data modeling concepts
• Familiarity with modern data platforms and tools (Databricks, Snowflake, ADF, etc.).
• Experience with data governance, data quality frameworks, and business rule standardization .
• Excellent communication and stakeholder management skills, with the ability to translate between business and technical audiences.
• Strong organizational skills with experience in Agile methodologies (backlog management, sprint planning, user story creation).

What Success Looks Like

• LOB analytics teams have a clear, prioritized roadmap for data initiatives.
• Business rules and definitions are standardized, governed, and documented across LOBs.
• Analytics teams are enabled to leverage the gold data layer for self-service without heavy reliance on engineering.
• The DMP backlog delivers high-value features with minimal downstream disruption .