Technical Product Owner, Enterprise POS

The Enterprise Data Architect is the design authority for Masco's enterprise POS and adjacent commercial data assets. This role owns the enterprise data model, the governed master data foundations, and the attribution logic that connects Business Unit, HQ, and retailer data into one trusted, reusable enterprise view. The Architect sets harmonization, data-modeling, and data-quality standards that the Data Engineering team executes against, and codifies those standards into documented, governed rules. This role is central to building a scalable, AI-ready enterprise data foundation.

What You'll Own

Enterprise Data Model & Master Data Foundations

• Own the enterprise data architecture blueprint and standardized data model, expandable across POS use cases.

• Own the governed master data foundations - Product, Retailer, Geographic, and Calendar.

• Establish and evolve the semantic and dimensional modeling framework used by BI and Engineering.

• Evaluate and lead implementation of master data management tools and processes to support the governed master data foundations.

Attribution & Harmonization

• Define and maintain the BU to HQ to retailer attribution crosswalk as the enterprise standard.

• Codify attribution logic into governed rules with clear ownership and change-approval processes.

• Approve hierarchy changes, mapping exceptions, and material structural changes.

• Actively curate product hierarchies and attribution mappings as ongoing operational activities, with the Business Data Analyst supporting attribution stewardship, use case validation, and downstream impact analysis.

Data Quality, Standards & Technical Governance

• Set harmonization, modeling, and data-quality standards, including validation guidelines.

• Enforce policies for data engineering, integration, security, and compliance.

• Monitor day-to-day data quality, investigate root causes, and drive resolution with Data Engineering and business stakeholders, with the Business Data Analyst supporting quality validation and consumer follow-up.

• Curate the enterprise data catalog, including definitions, lineage, and metadata, as an ongoing discipline.

Design Integrity for Requests, Enhancements & Delivered Work

• Serve as the design-authority checkpoint on incoming requests and enhancements, confirming work fits the enterprise model and standards.

• Approve architectural approaches before build begins.

• Partner with the Data Engineering Leader on delivery reviews and on escalated issues that touch the model, masters, or attribution.

• Provide design-integrity oversight of release management for engineering enhancements, requests, and projects, confirming released work aligns with the enterprise model and standards while the Data Engineering Leader owns the operational execution.

Technical Leadership & Investment Guidance

• Provide technical direction on masters, attribution, ingestion patterns, and modeling standards.

• Coach engineers on modeling discipline and enterprise design principles.

• Contribute to strategic planning, talent, tooling, and vendor evaluations.

• Contribute to budgeting and investment decisions for enterprise data platforms, services, and tools.

Documentation & Knowledge Management

• Own the documented definition of the enterprise data model, masters, and attribution rules.

• Establish documentation standards for models, attribution logic, and modeling decisions, in partnership with the Data Engineering Leader.

• Maintain the enterprise knowledge base and catalog for definitions, lineage, and material changes.

• Champion a "documented once, reused everywhere" culture across the team.

How You'll Partner (may not need in final posting but good context)

• Data Engineering Leader and Data Engineers: Provide specs, standards, and design authority. Partner on delivery reviews and issue resolution.

• BI Delivery Leader and BI Developers: Align on semantic layer and metric definitions built on the governed model.

• Technical Product Owner and Business Data Analyst: Partner on intake to confirm work fits the enterprise model.

• BU Analytics, HQ data stewards, and governance forums: Act as design authority in attribution and hierarchy governance.

• HQ IT, Security, and Architecture: Align enterprise data architecture with broader technology strategy.

• Bachelor's degree or higher in a related field, or equivalent professional experience.

• Substantial experience as an Enterprise Data Architect, Data Architect, or comparable role, including designing and governing enterprise data models across multiple business units or domains.

• Proven track record establishing master data, attribution, and harmonization standards in a multi-source, multi-brand environment.

• Experience leading and coaching technical data teams toward enterprise standards.

Skills & Competencies

• Design-authority mindset. Sets direction, holds the line on standards, and coaches others on why those standards matter.

• Strong strategic thinker who can move from ambiguity to a modeled, governed answer.

• Excellent communicator who can translate architecture decisions for both technical and business audiences.

• Detail-oriented and self-directed on complex, cross-functional problems.

• Flexible. Balances enterprise standards with pragmatic delivery.

• Collaborative across IT, Analytics, HQ, and Business Units.

• Continuous learner who stays current on emerging data practices.

Technical Understanding

• Strong understanding of enterprise data architecture and dimensional/semantic modeling (Kimball, Lakehouse patterns such as bronze/silver/gold).

• Working knowledge of modern cloud data platforms (Databricks, Azure data stack) and how to lead teams delivering against them.

• Familiarity with SQL, Python, ETL/ELT patterns, and BI semantic models sufficient to guide and evaluate engineering work.

• Understanding of data quality, catalog, lineage, and metadata management practices.

• Understanding of security, access, and compliance standards as they apply to enterprise data.

Preferred Qualifications

• Experience in retail, consumer goods, or manufacturing analytics environments where POS, sell-in, inventory, and third-party retail data are core.

• Prior experience designing master data, attribution logic, or product hierarchies across multiple retailers or channels.

• Familiarity with modern DataOps, Agile, or Kanban practices.

• Exposure to AI/ML-ready data architecture patterns (feature stores, governed semantic layers for AI/agentic consumption)., E-Verify Participation Poster: English & Spanish E-verify Right to Work Poster: English, Spanish