Job description
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.