Position Overview

The Principal Manufacturing & Semantic Architect is a critical leadership role responsible for defining and governing the canonical data and semantic model that underpins Hexion's industrial digital platform.

This role will establish how manufacturing assets, processes, materials, and data are consistently represented across:

• Plant systems (OT)
• Enterprise systems (IT)
• Cloud platforms
• AI/ML models
• Customer-facing applications

The successful candidate will bring deep expertise in industrial standards (ISA-95 / ISA-88) and translate complex manufacturing environments into scalable, structured data models that enable interoperability, analytics, and AI.

Key Responsibilities

1. Define and Govern the Canonical Manufacturing Data Model

Develop and maintain a standardized semantic model aligned with:

• ISA-95 (enterprise-control integration)
• ISA-88 (batch/process control)
• Emerging industry standards (e.g., CFIHOS where applicable)

Define core entities including:

• Assets, equipment hierarchies, and locations
• Materials, batches, and process segments
• Operational states, events, and relationships

Ensure consistent representation of manufacturing data across all systems.

2. Establish Semantic Standards and Data Contracts

Define and enforce:

• Data schemas
• API and event contracts
• Naming conventions and units of measure

Partner with engineering teams to ensure adherence across:

• Edge systems
• Cloud services
• Integration layers

Prevent semantic drift across teams, platforms, and external partners.

3. Define Semantic Meaning and Canonical Structure of AI Features

Define the semantic meaning and canonical structure of features used in predictive and optimization models. Establish what each feature represents in the context of manufacturing processes and operational data.

• Define feature-level semantic definitions grounded in manufacturing domain knowledge
• Ensure alignment between the meaning of training data and real-time operational data at the edge
• Collaborate with data science teams to ensure models reflect real-world process behavior

Note: The pipelines, storage, and lifecycle that deliver these features to AI models are owned by the Principal Industrial AI Data Architect.

4. Provide Semantic Translation Between OT, IT, and Digital Platforms

Serve as the authority on semantic and data model translation between:

• Plant floor systems (PLC, DCS, SCADA, historians)
• MES and ERP systems
• Cloud-based data and application platforms
• Ensure data models are both technically robust and operationally practical.

Note: Technical connectivity and protocol-level integration with OT systems are owned by the Principal Edge & OT Architect.

5. Support Platform Productization and External Solutions

Design semantic models that ensure the data model scales across tenants, including:

• Multiple manufacturing sites
• Multi-tenant environments
• External customer-facing products

Ensure extensibility and long-term maintainability of the data model.

Note: Data pipeline and access pattern design for multi-tenancy is owned by the Principal Industrial AI Data Architect.

6. Lead Governance and Continuous Evolution

Establish versioning and lifecycle management for:

• Data models
• Schemas
• Semantic definitions
• Facilitate cross-functional alignment across engineering, operations, and data teams.

Serve as the final authority on semantic architecture decisions.

7. Collaborate Across Teams

Partner with:

• Principal Edge & OT Architect (semantic model enforcement at the edge and OT data normalization)
• Principal Industrial AI Data Architect (feature semantics and data pipeline alignment)
• Platform Engineering (implementation of semantic standards in cloud services)
• Plant Operations and Process Engineering teams (domain validation and real-world grounding)

Ensure consistent execution across domains.

Key Competencies

• Strategic thinking with strong attention to detail
• Ability to translate complex systems into structured models
• Cross-functional leadership across OT, IT, and digital teams
• Strong communication and stakeholder alignment skills
• High ownership and accountability for architectural decisions

Minimum Qualifications

• Bachelor's degree in Engineering, Computer Science, Industrial Engineering, or related field (Master's preferred)
• 10+ years of experience in manufacturing systems, industrial automation, or process engineering
• 10+ years of experience in data modeling or system architecture in industrial environments
• Demonstrated expertise in ISA-95 and ISA-88 standards and manufacturing data structures and hierarchies
• Strong understanding of OT systems (PLC, DCS, SCADA, historians)
• Strong understanding of MES and ERP integration patterns
• Experience with relational and/or graph-based data modeling

Preferred Qualifications

Experience with:

• ISA or similar industry data standards
• Industrial IoT platforms or edge-to-cloud architectures
• AI/ML applications in manufacturing environments
• Cloud platforms (AWS preferred)

Familiarity with:

• Time-series data and event-driven architectures
• Data governance frameworks

Leadership Expectations

• Operate as a thought leader in industrial data and semantic architecture

• Influence without direct authority across multiple teams and partners

• Drive standards adoption across internal and external stakeholders

• Balance long-term architectural vision with near-term delivery needs

Work Environment & Travel

Travel to manufacturing sites and partner locations as needed (~10-25%).

Other