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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Industrial AI Data Architect - US Remote - **Company:** Hexion - **Location:** Columbus, OH, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Customer Data Management, Data Architecture, Data Governance, Extract Transform Load (ETL), Data Structures, Data Systems, Distributed Data Store, Cloud Services, Data Streaming, Systems Integration, Data Server Interface, Data Storage Technologies, Performance Testing, Real Time Systems, Feature Engineering, Data Ingestion, Data Strategy, Event Driven Architecture, Information Technology, Machine Learning Operations, Physical Data Models, Multiaccess Edge Computing, Software Version Control, Data Pipelines - **Published:** May 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8960ddbcee89d46d ## About the Role * 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 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, Experience with: * Industrial IoT or edge-to-cloud platforms * Manufacturing systems (OT + IT integration) * Cloud data platforms (AWS preferred) Familiarity with: * Streaming architectures * Event-driven systems * Data governance frameworks Other 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 ## Description 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: Edge systems cloud AI pipelines edge inference Define: * Data storage patterns (time-series, relational, event-based) * Data movement and transformation strategies Ensure architecture supports: * Real-time processing * Batch analytics * Model lifecycle integration 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 * Pipelines Ensure all data structures conform to: * Enterprise standards * Platform contracts Additional Job Responsibilities 4. Enable Scalable AI Model Integration Define data interfaces required by: * Internal AI teams * External model providers Support: * Model versioning * 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. 6. 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [When React Meets Reality: Building a Real-Time Control Room for Autonomous Vehicles](https://www.wearedevelopers.com/videos/2091-when-react-meets-reality-building-a-real-time-control-room-for-autonomous-vehicles) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Hacking Your Vacation: Using Data for Fun](https://www.wearedevelopers.com/videos/585-hacking-your-vacation-using-data-for-fun) - [Big Business, Big Barriers? 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