Enterprise Architect

The Enterprise Architect is the senior-most architect responsible for setting and governing the enterprise technology architecture for a global, multi-business manufacturing company. This role defines the target-state architecture, accelerates digital and operational excellence (ERP/MES/CRM/PLM/IIoT), and ensures secure, compliant solutions for both commercial and regulated environments.

The Enterprise Architect partners with product, operations, engineering, commercial, security, finance, and functional peers to deliver reliable, scalable, and cost-efficient platforms aligned to business strategy.

HOW YOU WILL MAKE AN IMPACT:

• Own the enterprise architecture (EA) strategy, reference architectures, and roadmaps across applications, data, integration, infrastructure, network, workplace, and security.
• Establish and run Architecture Steering Committee/Review Board, patterns/standards, and technology lifecycles.
• Balance standardization with BU autonomy; drive reuse and reduce duplication/technical debt.
• Architect the core industrial stack (ERP—e.g., SAP/Oracle; MES; PLM; QMS; WMS) and OT/IT convergence with IIoT, edge, and analytics.
• Enable smart factory capabilities (predictive maintenance, digital work instructions, traceability, SPC) and resilient global supply chain integration.
• Define multi-cloud strategy (Azure/AWS), hybrid patterns, landing zones, network segmentation, SASE/SD-WAN, and edge architectures.
• Lead API-first integration (iPaaS/ESB, event streaming), microservices, container platforms, and CICD guardrails.
• Own enterprise data architecture: MDM, metadata, lineage, lakehouse/warehouse, data mesh where appropriate, and data quality/governance.
• Partner with CISO to embed Zero Trust, identity, secrets management, PAM, and secure-by-design principles.
• Ensure architectures meet SOX, NIST 800-171/CMMC, DFARS View phone number on click.appcast.io, ITAR/EAR, ISO 27001, and customer contract requirements; segment and protect Controlled Unclassified Information (CUI) as required.
• Guide architecture for customer/partner portals, CPQ/CRM, e-commerce, service, field mobility, and analytics/AI platforms.
• Participate in governance for responsible AI/ML and model lifecycle (MLOps) in regulated and non-regulated contexts.
• Lead technology due diligence, integration blueprints, and post-close modernization for M&A activities.Rationalize application portfolio; manage vendor strategy and total cost of ownership.
• Translate strategy into executable roadmaps with milestones, budgets, and KPIs.
• Mentor a team of domain architects (app, data, integration, infrastructure, security); build an EA community of practice.
• Partner with Finance on cost optimization (cloud FinOps, license optimization).

WHAT YOU WILL BRING TO THE ROLE:

• BS in Computer Science/Engineering or related; MS/MBA preferred.
• 10+ years in architecture/engineering with 5+ years leading architecture teams in complex, global manufacturers; proven delivery at scale across ERP/MES/PLM and cloud.
• ERP (SAP/Oracle), MES, PLM, QMS; OT protocols and plant network segmentation.Azure/AWS architecture, landing zones, and infrastructure as code.
• Data platforms (lakehouse/warehouse), MDM, governance (catalog/lineage), analytics/AI.
• Zero Trust, IAM, SSO/MFA, PAM, EDR/XDR, data protection, logging/observability.
• Demonstrated experience with SOX ITGCs and regulated customer requirements (NIST 800-171/CMMC, DFARS, ITAR/EAR); contract flow-down comprehension.
• Executive presence, clear storytelling, ability to influence across engineering, operations, and finance; vendor and SOW negotiation.
• TOGAF or equivalent EA; Azure/AWS architect; CISSP/SABSA; ITIL; SAFe.
• Eligibility to work with export-controlled information; ability to obtain/maintain relevant clearances when required by customer programs.
• Willingness to visit manufacturing sites and suppliers globally.
• Demonstrated ability and willingness to use AI tools to improve productivity, decision-making, work quality, and to reduce costs. The successful candidate must be able to identify appropriate AI use cases and critically evaluate AI-generated outputs.