Principal Industrial AI Data Architect - US Remote

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Date:
Oct 26, 2026

Location:

Columbus, OH, US

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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 that:

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.

1. Define Industrial Data Architecture for AI

Design end-to-end data flows from:

Define:

• Data storage patterns (time-series, relational, event-based)
• Data movement and transformation strategies
• Real-time processing
• Batch analytics

2. Design Feature Pipelines and Delivery for AI Models

Design and govern the pipelines, storage, and lifecycle that build and deliver features 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

3. Integrate Semantic Model with Data Pipelines

Translate canonical semantic definitions into:

• Physical data models
• Schemas

Define data interfaces required by:

• Internal AI teams

Support:

• Feature compatibility
• Performance validation

5. 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.

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)

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