Data Product Lead/Engineer

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, the role ensures that data products built are architecturally aligned, properly modeled, governed, scalable, and usable across the organization. The DPO acts as the primary point of contact for Line of Business analytics teams, defines roadmaps and priorities, translates business requirements into actionable data product features, and enables self-service analytics by ensuring that 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.

Key Responsibilities

• Data Product Definition & Modeling – Define grain, primary keys, and conformed dimensions for gold-layer data products; author source-to-target mappings across bronze, silver, and gold transformations; partner with engineering on normalized vs dimensional modeling tradeoffs; validate modeling strategy; define data contracts between upstream ingestion pipelines and downstream consumers.
• Stakeholder Alignment & Roadmapping – Serve as the primary point of contact for analytics teams; define and manage the roadmap and priorities for data initiatives; provide updates on backlog progress, risks, dependencies, and timelines.
• Backlog Ownership & Delivery – Own and prioritize the Data Marketplace (DMP) backlog; translate business requirements into transformation logic, schema changes, acceptance criteria, and data quality rules; identify cross-domain dependencies; balance feature delivery with platform stability and technical debt; perform testing and validation to ensure high quality production data.
• Data Quality & Governance Enforcement – Ensure consistency of business rules and data definitions across multiple LOBs; embed governance requirements (RBAC, PII masking, compliance controls) into data products; surface and resolve conflicting business requirements; support root-cause analysis of data defects.
• Prototyping & Validation – Use SQL and Python to support just-in-time analysis and prototyping; develop lightweight prototypes demonstrating gold dataset usability; ensure datasets are analytics-ready and optimized for PowerBI / enterprise consumption.
• Cross Enterprise Alignment – Drive reuse of shared enterprise assets; facilitate resolution of conflicting business rules with governance teams; communicate and answer questions about upcoming releases.

Qualifications

• 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; 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.
• 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 AskHR@greystar.com.