Databricks Architect-Irvine, CA/Plano, TX/W2(GC)(USC)

Role:Databricks Architect
4x per week onsite 
Locations:  Irvine, CA or Plano, TX  

 
Top 3 Must-Haves
1. Deep Databricks Expertise
Candidate must possess significant hands-on experience with:

Databricks architecture
Databricks ecosystem
Data engineering within Databricks environments
2. Data Architecture & Data Modeling
Strong background designing:

Enterprise data architectures
Data models
Scalable analytical environments
3. Semantic Layer Experience
Experience:

Designing
Building
Standing up semantic layers
 
Required Qualifications

• 8+ years of experience in analytics architecture, BI solutions, semantic modeling, or data architecture.
• Deep expertise in dimensional modeling, star schema design, semantic layer design, business metrics standardization, and analytical data modeling.
  Strong experience designing and governing Silver and Gold layer architectures within a modern Lakehouse environment.
• Experience developing data models optimized for Power BI, Databricks, self-service analytics, and AI-powered analytics solutions.
• Strong experience with Power BI Semantic Models, Databricks Metric Views, Unity Catalog, or equivalent semantic technologies.
• Understanding of modern Lakehouse principles, including Bronze, Silver, and Gold data architecture patterns.
• Experience designing business-facing analytics products and self-service analytics solutions.
• Understanding of natural language query, conversational analytics, Agentic AI, and AI-enabled decision intelligence.
• Experience with Azure, Databricks, cloud-native analytics platforms, and enterprise BI ecosystems.
• Strong communication skills with the ability to bridge business and technical stakeholders
• Preferred Qualifications
• Experience with Microsoft Copilot, Databricks Genie, Agentic AI frameworks, AI agents, or Decision Intelligence platforms.
• Experience defining enterprise semantic layer strategies and KPI governance frameworks.
  Knowledge of data governance, metadata management, data lineage, data quality, and master data management.
• Experience implementing Databricks Lakehouse architecture and enterprise analytics modernization programs.
• Experience building analytics products that support AI copilots and conversational analytics experiences.
• Familiarity with Data Vault, dimensional modeling, medallion architecture, and data product design concepts Success Measures
• Increased adoption of self-service, conversational, and AI-assisted analytics.
• Increased percentage of business reporting powered by certified semantic models and governed data products.
• Reduced reliance on manual report development and ad hoc data preparation.
• Improved consistency of business metrics, KPI definitions, and semantic models across the enterprise.
• Improved quality, usability, and reusability of Silver and Gold layer data products.
• Increased business trust in data, analytics, and AI-generated insights.
  Faster time-to-insight and decision-making.
• Successful enablement of Agentic BI, conversational analytics, and AI-assisted decision intelligence.
• Increased alignment between Databricks Lakehouse architecture, semantic models, and business-facing analytics experiences.
• Reduction in duplicate datasets, conflicting metrics, and inconsistent analytical data models..