Strong background in Data Governance, Data Management, or Data Architecture.
Experience with Databricks, Unity Catalog, data lineage, and enterprise data platforms.
Expertise in KPI framework design, metric governance, and data modeling.
Experience establishing master data governance programs and enterprise data standards.
Knowledge of AI/ML data readiness, data quality assessment frameworks, and analytics platforms.
Ability to influence cross-functional stakeholders in a matrixed environment without direct authority.
Strong communication, leadership, and mentoring skills.
Responsibilities:
Trusted Data Foundation
Design, implement, and govern the enterprise trusted data foundation within Databricks, including Gold-layer data models, data quality controls, curated metrics views, and Unity Catalog lineage management.
KPI Taxonomy & Performance Data Modeling
Own and maintain the KPI taxonomy, establishing clear relationships between executive-level metrics, operational drivers, and root-cause indicators.
Develop and govern the enterprise performance data model, providing standardized definitions, time grains, dimensional hierarchies, and reporting structures across all supply chain domains.
Master Data Governance
Lead master data governance initiatives across product, supplier, customer, and compliance domains.
Establish standards, policies, and governance processes that improve data consistency, quality, and AI-enabled attribute discovery capabilities.
Metric Certification & Standardization
Define metric certification requirements and governance procedures for inclusion in official reporting.
Develop standardized KPI calculation frameworks, including actuals versus targets, rolling averages, period-over-period comparisons, and statistical process control methodologies.
Establish target and threshold management processes that support cascading business objectives, seasonal adjustments, and planning updates.
AI Readiness & Cross-Functional Data Governance
Lead enterprise Data Readiness for AI initiatives by developing assessment frameworks that evaluate completeness, accuracy, governance maturity, and automation readiness.
Enable natural language access to certified metrics through platforms such as ThoughtSpot, AtScale, Power BI, Looker, Tableau, or similar technologies.
Drive alignment on metric definitions, reporting standards, and measurement methodologies across business and technology teams.
Serve as a data governance subject matter expert in architecture reviews and mentor analysts, data engineers, and BI developers to promote sustainable governance practices.
