Job Description

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 www.greystar.com.

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

.