PXT VP Data Owner - Modeling Lead

1) Data Modeling & Structural Integrity

• Audit and govern relationships between data products to ensure entities, attributes, and models are well-structured and consistently defined across domains.
• Map cross-product dependencies and identify architecture gaps, redundancies, and misalignments.
• Ensure semantic consistency so shared concepts (e.g., customer, account, transaction) have consistent meaning, definitions, and lineage across domains.
• Partner with data engineers and data modelers to enforce standards for data quality, cleanliness, normalization, and maintainability.

2) Domain Coverage & Governance

• Maintain a comprehensive view of all active data domains, ensuring no domain is orphaned or underrepresented in the product taxonomy.
• Drive domain completeness reviews, identifying missing data products and surfacing gaps to leadership and domain owners.
• Establish and maintain data product classifications (e.g., authoritative/golden vs. derived, curated vs. raw, operational vs. analytical).

3) Stakeholder Partnership & Data Sourcing Strategy

• Partner with business stakeholders, data owners, and domain leads to ensure data lands in the right schemas for the right purposes.
• Develop and maintain data sourcing strategies: identify authoritative sources, resolve source conflicts, and plan for future acquisition/coverage.
• Facilitate data discovery sessions and working groups to validate product scope, align definitions, and close sourcing gaps.
• Translate complex technical concepts into clear business language and drive decisions to resolution.

4) Use Case Development & Business Alignment

• Apply a user-first approach to develop use cases tied to concrete business problems, translating stakeholder needs into actionable data product requirements.
• Conduct discovery interviews with business consumers to surface unmet needs and validate whether existing products are sufficient.
• Build and maintain a use-case repository aligned to strategic priorities to support roadmap planning and prioritization.
• Collaborate with analytics and product teams to validate that delivered data products drive measurable business outcomes.

Qualifications

• 5–8 years in data strategy, data architecture, data governance, or data product management within a complex enterprise environment.
• Demonstrated ability to read, interpret, and critique conceptual and logical data models; strong familiarity with ER modeling and metadata frameworks.
• Strong understanding of data governance principles: domains, ownership, stewardship, master/reference data, lineage, and controls.
• Proven track record translating business requirements, data product specifications.
• Experience working across multiple lines of business and domains in a matrixed environment.
• Familiarity with data cataloging/modeling and developer tools (e.g., Unity, AWS, Erwin, Databricks); SQL proficiency is a plus.