Data Product Lead/Engineer

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) is accountable for defining, governing, and evolving domain-aligned data products in alignment with Greystar’s data strategy. Partnering closely with Data Engineers and Architects, this role ensures that data products built are architecturally aligned, properly modeled, governed, scalable, 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. While the DPO does not build pipelines they are technically fluent in how data pipelines, ingestion patterns, and dimensional models operate within a modern lakehouse architecture.

JOB DESCRIPTION

Key Responsibilities:

Data Product Definition & Modeling

• Define grain, primary keys, and conformed dimensions for gold-layer data products.
• Author detailed source-to-target mappings across bronze → silver → gold transformations.
• Partner with engineering on normalized vs dimensional modeling tradeoffs.
• Validate modeling strategy (SCD types, surrogate keys, etc).
• Define data contracts between upstream ingestion pipelines and downstream consumers.

Stakeholder Alignment & Roadmapping

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

Backlog Ownership & Delivery

• Own and prioritize the Data Marketplace (DMP) backlog in alignment with engineering capacity, business needs and architectural constraints.
• Translate business requirements into transformation logic, schema changes, acceptance criteria, and data quality rules.
• Identify cross-domain dependencies across shared data assets.
• Balance feature delivery with platform stability and technical debt considerations.
• Perform testing and validation to ensure acceptance criteria is met and data deployed to production is of high quality and able to drive business value

Data Quality & Governance Enforcement

• Ensure consistency of business rules and data definitions across multiple LOBs; coordinate with Governance to resolve misalignments.
• Ensure governance requirements (RBAC, PII masking, compliance controls) are embedded into data products.
• Surface and escalate conflicting business requirements to governance teams, helping drive consensus.
• Support root-cause analysis of data defects in partnership with engineering.

Prototyping & Validation

• Use SQL and Python to support just-in-time analysis and prototyping for the analytics teams
• Develop lightweight prototypes demonstrating gold dataset usability.
• Ensure datasets are analytics-ready and optimized for Power BI / enterprise consumption.

Cross Enterprise Alignment

• Drive reuse of shared enterprise assets instead of LOB-specific duplications.
• Facilitate resolution of conflicting business rules with governance teams.
• Communicate and answer questions about upcoming releases and articulate business values the changes will enable.

What we're looking for:

• 5+ years in Data Product, Data Architecture, Analytics Engineering, or Data Engineering-adjacent roles.
• Strong organizational skills with experience in Agile methodologies (backlog management, sprint planning, user story creation).
• Excellent communication and stakeholder management skills, with the ability to translate between business and technical audiences.
• Advanced SQL proficiency; working Python knowledge.
• Strong understanding of dimensional modeling and normalization concepts.
• Experience with batch ETL design patterns and schema evolution strategies.
• Experience with Familiarity with modern data platforms and tools (Databricks, Snowflake, ADF, etc.).
• Familiarity with enterprise data governance and quality frameworks.

Important Notice: Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). If you receive suspicious requests, please report them immediately to [email protected].