Data Governance Lead

Data Governance Lead
Location: Sunnyvale, California (Onsite M-F)
Duration: 12-Month Contract + Possible Extension

Requirements:

• Data quality alerting and issue management- Own detection-to-resolution for data quality and abnormality issues in Databricks: alert coverage, triage, routing, SLAs, root-cause analysis, and closure. Hold engineers accountable for keeping DQ and abnormality checks current as pipelines and models change, covering standards, review cadence, and no silent drift on new or modified tables. Report DQ health and recurring failure patterns to stakeholders.
• Document Metrics, definitions, calculations, dimensions - Metric certification and standardization. Define certification standards and the governance gate metrics must pass before official reporting. OWN metric definitions, calculations, terms and measurement standards across functions, document & maintain these in our internal tool and mentor analysts, data engineers, and BI developers so standards become self-sustaining.
• Trusted data foundation- Involve with Analytics Engineers in Architecting and govern the Databricks Gold layer: certified data models with embedded DQ, abnormality checks, creating Metrics views & maintaining Data Lineage
• Data readiness for AI and cross-functional alignment - Involve in driving tiered assessment frameworks scoring datasets on completeness, accuracy, and automation readiness to determine safe levels of AI autonomy, and translate certified metrics into natural-language queryable experiences. Help in data quality improvements from Data Governance.

Here's the original JD:

Key Responsibilities

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

Required Skills & Experience

• 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.