Principal Specialist, Data Modeling & Arch (Operational Technology)

Why This Role Matters

OT Data Modeling Standards & Architecture

• Define enterprise OT data modeling standards for time-series data, events, alarms, assets, tags, measurements, and production contexts.
• Establish canonical OT concepts including asset hierarchy, location hierarchy, tag naming, measurement definitions, and work order linkage.
• Design scalable data schemas for curated OT layers and reusable OT data products with auditability, lineage, and quality attributes.
• Ensure modeling patterns address high-volume industrial data, latency requirements, site variability, and intermittent connectivity.

OT-to-IT Data Alignment & Integration

• Architect OT-to-IT data alignment patterns that connect operational measurements with enterprise entities for reporting and analytics.
• Align historians, SCADA/DCS, condition monitoring, maintenance, reliability, and production data with enterprise data products.
• Define integration rules for tag mapping, unit standardization, reference data, semantic consistency, and cross-site normalization.
• Support cross-domain analytics by ensuring OT data is usable for reliability, production performance, safety, sustainability, and cost insights.

Industrial Data Governance & Quality

• Embed governance-by-design into OT data models, including ownership, metadata, lineage, quality rules, and lifecycle controls.
• Define quality attributes for historian data, including compression, interpolation, quality flags, sampling frequency, and streaming patterns.
• Establish reusable governance patterns for site onboarding, data standardization, naming conventions, and master/reference data alignment.
• Ensure OT data products are traceable, reliable, maintainable, and aligned with platform architecture standards.

Security, Compliance & Solution Design Authority

• Partner with OT engineering, cybersecurity, data platform, and infrastructure teams to design secure and compliant OT data flows.
• Ensure OT data architecture respects segmentation, access control, operational resilience, and industrial cybersecurity requirements.
• Review solution designs, data models, vendor deliverables, and integration approaches to ensure architectural consistency.
• Resolve modeling trade-offs and guide design decisions to support long-term scalability, maintainability, and operational reliability.

Technical Leadership, Coaching & Knowledge Transfer

• Provide technical leadership on OT semantics, industrial data architecture, data modeling practices, and data product design.
• Coach engineers, data modelers, platform teams, and solution teams on OT modeling standards and implementation practices.
• Lead design reviews for critical OT data initiatives and ensure decisions are documented and reusable across sites.
• Build knowledge assets, standards, templates, and reference patterns to accelerate consistent OT data architecture delivery.

What Success Looks Like

Minimum Qualifications

• Bachelor's Degree in Computer Science, Computer Engineering, Data Engineering, Industrial Engineering, Information Systems, or a related discipline.
• Master's Degree is preferred.
• Minimum 10 years of progressive experience in data modeling, data architecture, data engineering, industrial data platforms, or OT/IT integration.
• Strong exposure to operational technology data sources such as historians, SCADA/DCS, condition monitoring, maintenance, reliability, and production systems.
• Demonstrated experience defining enterprise data models, data standards, semantic layers, governance controls, and scalable data product architectures.

Skills That Matter

• OT data modeling and industrial data architecture.
• Time-series data, historians, SCADA/DCS, events, alarms, and tag structures.
• Asset hierarchy, location hierarchy, measurement models, and semantic standardization.
• OT-to-IT integration and enterprise data alignment.
• Data product design, curated data layers, metadata, lineage, and quality management.
• Data governance, reference data, naming conventions, and unit standardization.
• Streaming ingestion, high-volume data handling, and scalable schema design.
• Industrial cybersecurity awareness and secure OT data flow design.
• Architecture review, stakeholder collaboration, coaching, and technical documentation.