Principal Specialist, Engineering and Extract, Transform, Load (Operational Technology)

Why This Role Matters

OT Data Engineering Strategy & Architecture

• Establish enterprise standards, frameworks, and reference architectures for OT data ingestion, transformation, and integration.
• Define scalable patterns that enable consistent access to operational data across sites, systems, and platforms.
• Strengthen alignment between OT data engineering practices, enterprise architecture, and governance objectives.

Data Pipeline Reliability & Performance

• Deliver resilient OT data pipelines that ensure reliable, timely, and accurate operational data availability.
• Improve data consistency through robust handling of reconciliation, latency, backfill, quality validation, and exception management.
• Enhance platform performance through optimization, observability, and operational resilience initiatives.

Data Governance & Security

• Embed governance, lineage, auditability, and quality controls throughout the OT data lifecycle.
• Strengthen compliance with OT security, segmentation, and enterprise data governance requirements.
• Ensure operational data remains secure, traceable, and aligned with enterprise risk management standards.

Data Products & Platform Enablement

• Enable the creation of trusted OT data products that support analytics, reporting, operational monitoring, and AI use cases.
• Drive standardization of data models, tag structures, quality indicators, and transformation logic across industrial environments.
• Improve usability and accessibility of operational data for business and technical stakeholders.

Technical Leadership & Assurance

• Provide architectural guidance for complex OT integration and engineering initiatives across multiple sites.
• Govern engineering quality by establishing technical standards, design reviews, and delivery assurance mechanisms.
• Strengthen delivery consistency across internal teams, partners, and vendors through expert technical oversight.

Innovation & Continuous Improvement

• Advance OT data engineering capabilities through adoption of modern engineering practices and technologies.
• Improve efficiency, scalability, and cost effectiveness of OT workloads through continuous enhancement initiatives.
• Promote engineering excellence and knowledge sharing across the enterprise data ecosystem.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Data Engineering, Industrial Engineering, or a related discipline.
• Master's degree preferred.
• Exposure to Operational Technology (OT) systems is advantageous.

Experience

• 10-15 years of experience in data engineering, ETL, and enterprise data integration environments.
• Experience working within OT, industrial, manufacturing, mining, or operational data ecosystems.
• Proven experience designing and implementing historian and OT system integration solutions at enterprise scale.
• Experience establishing engineering standards, governance frameworks, and scalable data delivery practices.

Skills That Matter

• OT Data Engineering; ETL & Data Integration Architecture; Industrial Historians & Time-Series Data Management.
• Data Quality & Lineage Management; OT-to-IT Integration Design; Enterprise Data Product Development.
• Data Pipeline Reliability Engineering; Platform Performance Optimization; Data Governance & Security.
• Solution Architecture & Technical Assurance; Vendor Management & Delivery Oversight; Enterprise Data Strategy Execution.
• Stakeholder Management; Cross-Functional Technical Leadership; Influencing & Decision Making.
• Vendor & Partner Collaboration; Communication & Knowledge Sharing; Change Leadership.