Location: Sunnyvale, CA (Onsite)
Job Type: W2
Experience: 10+ Years
We are seeking an experienced Senior Data Governance Architect to lead enterprise data governance, data architecture, KPI standardization, and AI data readiness initiatives.
The ideal candidate will have strong expertise in Databricks, Unity Catalog, enterprise data modeling, Master Data Management (MDM), and data quality frameworks. This role involves architecting trusted data foundations, establishing standardized KPI frameworks, and ensuring enterprise datasets are governed, reliable, and ready to support AI-driven applications and analytics.
The candidate will work closely with cross-functional business and technology teams to establish consistent data definitions, governance standards, and performance measurement frameworks across the organization.
Key Responsibilities1. Data Architecture & Trusted Data Foundation
Architect and govern enterprise data foundations using Databricks.
Design and maintain Gold-layer data models with appropriate data quality checks, metrics views, and validation frameworks.
Implement and manage data lineage, metadata, and governance using Unity Catalog.
Ensure data accuracy, consistency, completeness, and reliability across enterprise data platforms.
2. KPI Taxonomy & Performance Data Modeling
Own and maintain enterprise KPI taxonomy, connecting executive-level metrics with operational drivers and root-cause indicators.
Define unified performance data models representing supply chain KPIs across multiple business domains.
Establish consistent entity definitions, time grains, dimensional hierarchies, and measurement standards.
Develop frameworks for measuring operational performance, including OTIF, total cost, and quality metrics.
3. Master Data Governance
Lead enterprise master data governance initiatives across product, supplier, customer, and compliance domains.
Establish data governance policies, standards, ownership, and quality controls.
Enable AI-powered attribute discovery and improve master data consistency across enterprise systems.
4. Metric Certification & Standardization
Define metric certification standards and governance processes for official enterprise reporting.
Design standardized KPI computation frameworks covering actuals vs. targets, rolling averages, period-over-period comparisons, and statistical process control.
Develop threshold and target management frameworks to support cascading business goals from executive leadership to operational teams.
Establish governance mechanisms for seasonal adjustments, planning changes, and performance measurement consistency.
5. AI Data Readiness & Cross-Functional Collaboration
Lead Data Readiness for AI initiatives by developing frameworks to assess datasets based on completeness, accuracy, quality, and automation readiness.
Establish data readiness standards to determine the suitability of datasets for AI-powered applications and autonomous workflows.
Enable natural-language querying of certified metrics through platforms such as Metamate.
Collaborate with business stakeholders, data engineers, analysts, and BI developers to establish enterprise-wide data standards.
Provide technical leadership during architecture reviews and mentor technical teams to ensure sustainable data governance practices.
Strong experience in Enterprise Data Governance and Data Architecture.
Hands-on experience with Databricks data platform and Gold-layer data modeling.
Strong knowledge of Unity Catalog, data lineage, metadata management, and governance.
Experience designing enterprise data models and KPI/performance measurement frameworks.
Strong understanding of Master Data Management (MDM) across product, supplier, customer, and compliance domains.
Experience developing data quality frameworks, validation standards, and metric certification processes.
Strong SQL and data modeling skills.
Experience with enterprise KPI taxonomy, metric standardization, and target management.
Understanding of AI/ML data readiness, data quality assessment, and AI-enabled analytics.
Excellent stakeholder management, communication, analytical, and problem-solving skills.
Ability to influence cross-functional teams and drive enterprise-wide governance standards.
Experience with supply chain data and operational performance metrics such as OTIF, total cost, and quality index.
Experience with AI-powered data discovery and natural-language analytics platforms such as Metamate.
Knowledge of statistical process control, rolling averages, and period-over-period performance measurement.
Experience establishing enterprise data governance operating models.
Experience mentoring data engineering, analytics, and BI teams.
Candidates with experience in the following areas are encouraged to apply:
Senior Data Governance Architect
Enterprise Data Architect
Databricks Data Architect
Data Management Architect
Master Data Management Lead
Data Strategy Architect
Supply Chain Data Architect
Interested candidates with strong Databricks, Unity Catalog, Enterprise Data Governance, MDM, and AI Data Readiness experience are encouraged to apply.
