Principal Industrial AI Data Architect - US Remote

Imagine Everything. Build the Future with Hexion. At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress—developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future. This is where bold thinkers, problem-solvers, and innovators come together to shape what’s next. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward.

About the Role

The Principal Industrial AI Data Architect is responsible for designing and governing the data architecture that enables reliable, scalable AI across industrial environments. This role ensures:

• Data pipelines are aligned with the canonical semantic model
• Features used in AI models are consistent across training and runtime
• Industrial data is structured for real-time inference and long-term analytics

This role is the bridge between data, semantics, and AI execution.

Responsibilities

• Define Industrial Data Architecture for AI
  • Design end-to-end data flows from edge systems → cloud → AI pipelines → edge inference
  • Define data storage patterns (time-series, relational, event-based)
  • Establish data movement and transformation strategies
  • Ensure architecture supports real-time processing, batch analytics, and model lifecycle integration
• Design Feature Pipelines and Delivery for AI Models
  • Design and govern pipelines, storage, and lifecycle for features delivered to AI models, based on canonical definitions established by the Principal Manufacturing & Semantic Architect
  • Define feature engineering pipelines for both training (cloud) and inference (edge) environments
  • Ensure consistency between training datasets and runtime inference data
  • Prevent feature drift and data mismatch through automated validation
• Integrate Semantic Model with Data Pipelines
  • Translate canonical semantic definitions into physical data models, schemas, and pipelines
  • Ensure all data structures conform to enterprise standards and platform contracts
• Enable Scalable AI Model Integration
  • Define data interfaces required by internal AI teams and external model providers
  • Support model versioning, feature compatibility, and performance validation
• Design for Multi-Tenant and Product Use Cases
  • Ensure data pipelines and access patterns support multi-tenant environments, including:
    • Customer data isolation and secure access controls
    • Scalable onboarding of new tenants and use cases
    • Reuse of data pipelines across customers and deployments

  Note: The underlying data model for multi-tenancy is governed by the Principal Manufacturing & Semantic Architect.

• Collaborate Across Teams
  • Partner with:
    • Principal Manufacturing & Semantic Architect (canonical model definition and feature semantics)
    • Principal Edge & OT Architect (edge data ingestion and inference data requirements)
    • Platform Engineering (implementation and infrastructure)
    • AI/Data Science teams (model requirements and validation)
  • Ensure consistent execution across domains

Qualifications

• Minimum Qualifications
  • Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred)
  • 10+ years of experience in data architecture, industrial data systems, or IoT platforms
  • Strong experience with time-series data (e.g., historian systems), data pipelines, and ETL/ELT
  • Strong experience with distributed data systems
  • Understanding of AI/ML data requirements and feature engineering concepts
• Preferred Qualifications
  • Experience with industrial IoT or edge-to-cloud platforms
  • Experience with manufacturing systems (OT + IT integration)
  • Experience with cloud data platforms (AWS preferred)
  • Familiarity with streaming architectures, event-driven systems, and data governance frameworks

Skills

• Strong system design and data modeling skills
• Ability to connect business, operational, and AI requirements
• High attention to data consistency and integrity
• Cross-functional collaboration

Leadership Expectations

• Operate as a thought leader in industrial data architecture and AI data strategy
• Influence without direct authority across multiple teams and partners
• Drive standards adoption for data pipelines and AI data practices across internal and external stakeholders
• Balance long-term architectural vision with near-term delivery needs

Schedule

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