Data Architect

Orion Innovation•Edison, NJ

About The Position

Orion Innovation is seeking an experienced Data Architect to lead architecture, analytics, and AI-ready data strategy for enterprise platforms. The role combines expertise in Databricks, Microsoft Fabric, knowledge graphs, and ontologies with architectural leadership, strategic thinking, and client-facing communication. The Data Architect will define a data and AI vision, turn it into an actionable technology roadmap, and lead architecture teams in delivering scalable solutions. The position leads the design and evolution of enterprise data and analytics platforms, including scalable data processing, integration, transformation, and analytical solutions. It establishes platform standards and reference architectures and guides teams on reliable, secure, high-performance design. It also defines how enterprise data can be organized and contextualized for AI agents, using structured and unstructured data, semantic technologies, retrieval approaches, and reusable integration patterns. The role owns the long-term data platform and AI-enablement vision, evaluates current capabilities, identifies gaps, and creates phased roadmaps aligned with business priorities. As a senior client-facing technology leader, the Data Architect works with client architects, technology leaders, and business stakeholders, communicates architectural recommendations and trade-offs, and mentors data architects while providing technical direction and governance.

Requirements

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 and capabilities, and its 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.

Nice To Haves

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.

Responsibilities

Lead the architecture and evolution of enterprise-scale data and analytics platforms.
Design scalable data processing, integration, transformation, and analytical solutions using Azure Databricks and Microsoft Fabric.
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 data platform challenges involving scalability, performance, governance, and interoperability.
Define how enterprise data can be organized, contextualized, and made accessible to AI agents and agentic applications.
Design approaches combining structured and unstructured enterprise data to provide business context to AI agents.
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.
Architect large-scale data analytics solutions using Databricks, including distributed processing, Delta Lake, and Lakehouse architectures.
Drive performance optimization, scalability, reliability, and cost efficiency across data platforms.
Evaluate emerging data technologies and recommend adoption based on business value and architectural fit.
Own and articulate the long-term data platform and AI-enablement vision.
Assess existing capabilities, identify architectural gaps, and define future-state architectures.
Develop phased technology roadmaps aligned with business priorities and balance strategic direction with delivery considerations.
Identify opportunities to evolve traditional analytics platforms into intelligent, AI-enabled enterprise data ecosystems.
Engage client architects, technology leadership, and business stakeholders as a senior client-facing technology leader.
Communicate complex architectural concepts, technology recommendations, and trade-offs to technical and non-technical audiences.
Lead and mentor 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 technical credibility and a clear vision.