Databricks Architect
Role Summary
The Databricks architect will define and govern the analytical data models and architecture for the Silver and Gold layers of the Databricks Lakehouse, ensuring that business-ready, consumable, and trusted data products are consistently delivered across the enterprise.
They will design and deliver the next generation of analytics experiences that enable business users to interact with data through dashboards, natural language, AI copilots, and intelligent agents. This role is responsible for establishing the enterprise semantic layer and business metrics framework that powers trusted self-service analytics, conversational BI, and agentic decision intelligence.
Working closely with business, analytics, data engineering, and AI teams, this leader will translate business needs into scalable analytics solutions that increase insight accessibility, decision speed, and trust in data.
Key Responsibilities
Semantic Layer & Analytics Architecture

Design and own the enterprise semantic layer, including common business metrics, KPIs, hierarchies, dimensions, and analytical definitions.
Create scalable semantic models that serve as the foundation for reporting, self-service analytics, conversational BI, AI copilots, and agentic analytics experiences.
Establish enterprise standards for metric governance, semantic model design, usability, performance, scalability, and consistency across business domains.
Define and manage certified business metrics and KPI frameworks that support a single version of the truth.

Lakehouse Data Architecture & Modeling

Define the architectural standards and analytical data models for the Silver and Gold layers of the Databricks Lakehouse.
Design business-focused dimensional, star-schema, denormalized, and analytical data models optimized for reporting, self-service analytics, AI consumption, and semantic layer integration.
Partner with Data Engineering teams to ensure Bronze-to-Silver-to-Gold transformation patterns align with enterprise architectural standards and business requirements.
Establish standards for reusable data products, conformed dimensions, business entities, reference data, and master data integration.
Ensure Silver-layer models support reusable, governed analytical datasets while Gold-layer models are curated and optimized for business consumption and decision-making.
Drive consistency of analytical data structures across Power BI, Databricks, AI agents, and self-service analytics platforms.
Collaborate with Data Governance teams to ensure architecture incorporates metadata, lineage, quality controls, business definitions, and regulatory requirements.

Agentic BI & Conversational Analytics

Define and deliver Agentic BI solutions that help users discover insights, answer questions, identify trends, and accelerate decisions.
Enable natural language analytics through semantic modeling, metadata design, ontologies, and business-friendly data structures.
Develop architectures that support AI copilots, conversational BI, and autonomous analytics workflows.
Evaluate and implement modern capabilities including Microsoft Copilot, Databricks Genie, AI agents, and Decision Intelligence solutions.

Business Partnership & Analytics Enablement

Partner with business stakeholders to translate decision-making needs into intuitive analytics products and experiences.
Collaborate with Analytics Product Managers, business SMEs, and domain leaders to define metrics, KPIs, and analytical requirements.
Drive adoption of analytics products through usability, trust, consistency, business alignment, and user experience design.
Act as a trusted advisor to business and technology leaders on analytics architecture, semantic modeling, and AI-enabled analytics capabilities.

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..

Databricks Architect

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