Databricks Engineer

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..

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