Principal Industrial AI Data Architect - US Remote

Hexion•Worthington, OH

12d•Remote

About The Position

The Principal Industrial AI Data Architect designs and governs data architecture that enables reliable, scalable AI across industrial environments. The role bridges data, semantics, and AI execution by ensuring data pipelines align with the canonical semantic model, AI features remain consistent between training and runtime, and industrial data supports real-time inference and long-term analytics. The architect designs end-to-end data flows from edge systems through the cloud and AI pipelines to edge inference; defines storage, movement, and transformation strategies; and ensures support for real-time processing, batch analytics, and model lifecycle integration. The role also governs feature pipelines and delivery, translates canonical semantic definitions into physical models, schemas, and pipelines, and establishes data interfaces and validation practices for AI model integration. The position supports multi-tenant products through isolated, secure customer data access, scalable tenant onboarding, and reusable pipelines. It collaborates with semantic and edge architects, platform engineering, and AI/data science teams. The role calls for thought leadership, cross-team influence, adoption of data standards, and balancing long-term architecture with delivery needs. Travel to manufacturing sites and partner locations is expected as needed, approximately 10–25%.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
10+ years of experience in data architecture, industrial data systems, or IoT platforms.
Strong experience with time-series data, such as historian systems, data pipelines, and ETL/ELT.
Strong experience with distributed data systems.
Understanding of AI/ML data requirements and feature engineering concepts.
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 skills.
Ability to influence without direct authority across multiple teams and partners.
Ability to drive standards adoption for data pipelines and AI data practices across internal and external stakeholders.
Ability to balance long-term architectural vision with near-term delivery needs.

Nice To Haves

Master's degree.
Experience with industrial IoT or edge-to-cloud platforms.
Experience with manufacturing systems and OT/IT integration.
Experience with cloud data platforms; AWS is preferred.
Familiarity with streaming architectures.
Familiarity with event-driven systems.
Familiarity with data governance frameworks.

Responsibilities

Design end-to-end data flows from edge systems to cloud, AI pipelines, and edge inference.
Define data storage patterns, including time-series, relational, and event-based storage, along with data movement and transformation strategies.
Ensure architecture supports real-time processing, batch analytics, and model lifecycle integration.
Design and govern pipelines, storage, and lifecycle for building and delivering AI model features, based on canonical definitions established by the Principal Manufacturing & Semantic Architect.
Define feature engineering pipelines for cloud-based training and edge inference environments.
Ensure consistency between training datasets and runtime inference data, and prevent feature drift and data mismatch through automated validation.
Translate canonical semantic definitions into physical data models, schemas, and pipelines, ensuring conformity with enterprise standards and platform contracts.
Define data interfaces for internal AI teams and external model providers, and support model versioning, feature compatibility, and performance validation.
Ensure pipelines and access patterns support multi-tenant environments, secure customer data isolation, scalable tenant and use-case onboarding, and pipeline reuse.
Collaborate with the Principal Manufacturing & Semantic Architect, Principal Edge & OT Architect, Platform Engineering, and AI/Data Science teams to ensure consistent execution across domains.
Provide thought leadership in industrial data architecture and AI data strategy; influence teams and partners, drive standards adoption, and balance architectural vision with near-term delivery needs.
Travel to manufacturing sites and partner locations as needed, approximately 10–25%.