Sr. Architect, Enterprise

Sr. Architect, Enterprise

The Sr. Architect, Enterprise serves as the organization's principal architecture leader responsible for defining and governing enterprise technology architecture with a strategic focus on Data, Artificial Intelligence (AI), Enterprise Integration, and Digital Product Architecture. This role establishes the long-term technology vision, target-state architectures, architectural standards, governance frameworks, and modernization roadmaps that enable scalable, secure, interoperable, and AI-ready business capabilities across the enterprise.

Operating within an Azure-first, Databricks-centric environment, the Senior Enterprise Architect provides leadership across enterprise applications, cloud platforms, data platforms, lake house architectures, artificial intelligence solutions, machine learning platforms, automation technologies, APIs, integration services, and digital products. The role is accountable for ensuring all technology investments align with enterprise architecture principles, cybersecurity requirements, regulatory obligations, operational objectives, and business strategy.

As a data-first and AI-first organization, the Sr. Architect, Enterprise is responsible for driving the evolution of enterprise data architecture, data products, canonical data models, AI-ready information assets, intelligent automation platforms, and modern integration capabilities that support analytics, machine learning, generative AI, agentic AI, robotic process automation (RPA), and enterprise decision intelligence. The position partners closely with executive leadership, business stakeholders, software engineering, data engineering, cloud engineering, cybersecurity, compliance, analytics, and operational teams to ensure that technology solutions are scalable, resilient, governed, and aligned with enterprise strategic objectives.

How do I make an impact on my team?

• Serve as the organization's principal architecture authority across enterprise applications, data platforms, AI platforms, integrations, cloud services, and digital products.
• Develop and maintain enterprise architecture principles, standards, reference architectures, governance frameworks, and target-state roadmaps.
• Establish and govern technology standards supporting software engineering, data engineering, cloud engineering, integration engineering, and AI initiatives.
• Lead architecture review boards and provide architectural oversight for strategic technology initiatives.
• Evaluate emerging technologies and develop recommendations supporting enterprise modernization, innovation, scalability, and operational efficiency.
• Drive technology rationalization efforts to reduce complexity, eliminate redundancy, and improve interoperability across enterprise systems.
• Define and govern enterprise data architecture strategy, including lake house architecture, data products, canonical data models, master data management, metadata management, and enterprise data governance.
• Establish enterprise standards for Azure Databricks, Delta Lake, Unity Catalog, Azure Data Lake Storage Gen2, Data Ops, and AI-ready data platforms.
• Lead architecture decisions supporting machine learning, predictive analytics, generative AI, retrieval-augmented generation (RAG), vector databases, semantic search, agentic AI, and intelligent automation.
• Define enterprise AI architecture standards including model governance, responsible AI, AI security, AI operations, and AI platform integration patterns.
• Guide the design of enterprise data products supporting analytics, automation, operational reporting, and business intelligence initiatives.
• Ensure enterprise data assets remain secure, governed, discoverable, reusable, and compliant with regulatory requirements.
• Define the enterprise intelligence architecture strategy supporting analytics, business intelligence, machine learning, generative AI, agentic AI, intelligent automation, and decision intelligence capabilities.
• Establish enterprise standards for AI-ready data products, semantic layers, knowledge management, vector stores, retrieval-augmented generation (RAG), AI orchestration frameworks, and agent architecture.
• Design reference architectures for AI assistants, AI copilots, intelligent workflow automation, agentic AI solutions, predictive analytics, and enterprise decision support systems.
• Lead the implementation of enterprise AI platforms utilizing Azure AI Services, Azure OpenAI, Databricks AI, Azure Machine Learning, MLflow, vector databases, and related technologies.
• Define governance frameworks supporting responsible AI, model lifecycle management, AI security, model monitoring, explainability, and regulatory compliance.
• Partner with business leaders to identify opportunities to transform operational processes into intelligence-driven workflows utilizing AI, machine learning, automation, and advanced analytics.
• Establish architectural patterns supporting human-in-the-loop AI, autonomous agents, intelligent document…