Principal Enterprise Architect

Enterprise Architecture serves as the foremost technical authority and strategic leader for our AI discipline.

This is role is an Enterprise Architect who combines profound technical expertise with the ability to influence and guide the entire organization's technical direction.

The Principal Architect will be entrusted with our most complex, ambiguous, and highest-impact technical challenges.

You will operate across multiple teams and business units, setting a long-term vision for our AI architecture, platforms, and practices.

Lead AI & Cloud architectures to future target-state architecture for a cloud-native, AI-enabled data/API ecosystem.

This leader will define pragmatic reference architectures, patterns, and guardrails that accelerate delivery—shifting architecture from a “governance gate” to a product-minded enablement function that empowers teams to build secure, compliant, and scalable data products faster.

Your day to day

• Define and drive the long-term Enterprise Architecture technical roadmaps for AI at the company. Identify and champion new technological paradigms that create step-change opportunities and sustainable competitive advantages.
• Design, and prototype foundational AI platforms, frameworks, and core services that will be leveraged by engineering teams across the entire organization. Your designs will set the standard for scalability, security, and innovation.
• Legacy Modernization & Target-State Architecture: Lead modernization of legacy data platforms into a cloud-native architecture, defining migration strategies, transitional architectures, and decommissioning roadmaps.
• Cloud Data Platforms (Warehouse/Lakehouse): Architect enterprise solutions using modern data warehousing and lakehouse platforms, including workload placement, cost/performance optimization, and resilience patterns.
• Data Engineering Enablement: Establish patterns for ingestion, transformation, orchestration, and observability (batch/streaming), and partner with engineering teams to improve delivery quality and velocity.
• AI-Driven Data Modernization: Apply AI concepts to modernize data engineering and delivery (e.g., intelligent metadata, automated lineage, code assist, testing/validation automation, anomaly detection, and policy enforcement).
• Guardrails, Standards & Reusable Blueprints: Define architecture guardrails, reference implementations, reusable templates, and “paved roads” that teams can adopt with minimal friction.
• Governance-by-Design: Embed security, privacy, risk, and regulatory requirements into architecture patterns and CI/CD practices enabling “continuous compliance” rather than after-the-fact reviews.