Establish architectural standards, best practices, and reference architectures for data platforms.
Guide data architects and engineering teams in designing reliable, secure, high-performance solutions.
Address architectural challenges involving data scalability, performance, governance, and interoperability.
2. AI-Ready Data & Context Engineering
Define how enterprise data can be organized, contextualized, and made accessible to AI agents and agentic applications.
Demonstrate strong understanding of ontologies, knowledge graphs, semantic models, and metadata-driven architectures.
Design approaches that combine structured and unstructured enterprise data to provide meaningful business context to AI agents.
Understand and apply concepts such as RAG, GraphRAG, semantic retrieval, and context engineering.
Establish reusable patterns for exposing trusted enterprise data to AI agents through APIs, tools, and other integration mechanisms.
3. Data Analytics & Platform Scalability
Architect large-scale data analytics solutions using Databricks, including distributed processing, Delta Lake, and Lakehouse architectures.
Understand Microsoft Fabric capabilities, including OneLake, Lakehouse, Data Warehouse, and semantic models.
Drive performance optimization, scalability, reliability, and cost efficiency across data platforms.
Evaluate emerging data technologies and recommend their adoption based on business value and architectural fit.
4. Strategy, Vision & Roadmap
Own and articulate the long-term data platform and AI-enablement vision.
Assess existing capabilities, identify architectural gaps, and define future-state architectures.
Develop practical, phased technology roadmaps aligned with business priorities.
Identify opportunities to evolve traditional analytics platforms into intelligent, AI-enabled enterprise data ecosystems.
Balance strategic architectural direction with pragmatic delivery considerations.
5. Client Engagement & Team Leadership
Act as a senior client-facing technology leader, engaging with client architects, technology leadership, and business stakeholders.
Clearly communicate complex architectural concepts, technology recommendations, and trade-offs to technical and non-technical audiences.
Lead and mentor a team of data architects, providing technical direction and architectural governance.
Facilitate architecture discussions, design reviews, and strategic planning sessions.
Influence architectural decisions and build stakeholder confidence through strong technical credibility and a clear vision.
Required Qualifications & Experience
15+ years of experience in data engineering, data architecture, enterprise analytics, or related technology domains.
Proven experience leading enterprise-scale data architecture and modernization initiatives.
Deep hands-on architectural expertise in Azure Databricks, including Lakehouse architecture, Delta Lake, Spark, and large-scale data processing.
Strong working knowledge of Microsoft Fabric, its architecture, capabilities, and integration with enterprise data ecosystems.
Solid understanding of ontologies, knowledge graphs, semantic technologies, and enterprise data modeling.
Demonstrated understanding of how enterprise data supports generative AI, AI agents, and context-aware intelligent applications.
Experience designing scalable, secure, and governed enterprise data platforms.
Proven ability to define technology strategy, target-state architecture, and phased implementation roadmaps.
Experience leading teams of data architects and technical specialists.
Excellent client-facing communication, presentation, stakeholder management, and technical storytelling skills.
Ability to work independently, challenge existing approaches, and recommend innovative yet practical solutions.
Preferred Qualifications
Experience with Microsoft Azure data and AI services, including Azure AI Search, Microsoft Foundry, and related services.
Knowledge of Microsoft Fabric IQ, semantic layers, and emerging enterprise context platforms.
Experience implementing knowledge graphs or ontology-driven enterprise data solutions.
Familiarity with agentic AI frameworks, MCP, and enterprise AI integration patterns.
Experience with complex, globally distributed enterprise environments, particularly regulated industries.
Experience developing enterprise data strategies and presenting them to senior technology executives.
What Success Looks Like
A clear, compelling Data & AI Architecture Vision aligned with business objectives.
A practical roadmap for evolving existing Databricks and Microsoft Fabric capabilities.
A scalable architectural approach for making trusted enterprise data available to AI agents.
Strong architectural leadership and consistent technical direction across data engineering teams.
Recognition as a trusted architectural advisor to client technology leadership.
Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
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