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

Job Description Why This Role Matters The Principal Specialist, Engineering and ETL (OT) enables Maaden to unlock the value of operational technology data by establishing scalable, secure, and reliable OT data engineering capabilities across the enterprise. The role ensures that operational data from industrial systems is transformed into trusted, accessible, and analytics-ready information that supports operational performance, decision-making, and digital transformation initiatives. The role strengthens enterprise data foundations by defining engineering standards, governance requirements, and integration patterns that enable consistent delivery of high-quality OT data products across multiple sites and operating environments. Through robust pipeline design and architectural leadership, the role improves data availability, reliability, and transparency while maintaining security and compliance requirements. By bridging operational technology environments with enterprise data platforms, the role accelerates analytics adoption, operational insight generation, and AI-driven innovation, enabling Maaden to build a sustainable and governed industrial data ecosystem. What You Will Deliver 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. What Success Looks Like • OT data is consistently available, trusted, and accessible across enterprise analytics and operational platforms. • Data pipelines operate with high levels of reliability, resilience, and performance across multiple sites. • Governance, lineage, security, and auditability requirements are embedded within OT data engineering processes. • Operational data products support business decision-making, performance management, and advanced analytics initiatives. • OT-to-IT integration standards are adopted consistently across projects and operating environments. • Engineering quality, delivery consistency, and platform scalability improve through defined standards and governance controls. 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 Functional Expertise • 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 Business & Delivery • Data Pipeline Reliability Engineering • Platform Performance Optimization • Data Governance & Security • Solution Architecture & Technical Assurance • Vendor Management & Delivery Oversight • Enterprise Data Strategy Execution People & Collaboration • Stakeholder Management • Cross-Functional Technical Leadership • Influencing & Decision Making • Vendor & Partner Collaboration • Communication & Knowledge Sharing • Change Leadership Show more Show less

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. What Success Looks Like: OT data is consistently available, trusted, and accessible across enterprise analytics and operational platforms. Data pipelines operate with high levels of reliability, resilience, and performance across multiple sites. Governance, lineage, security, and auditability requirements are embedded within OT data engineering processes. Operational data products support business decision-making, performance management, and advanced analytics initiatives. OT-to-IT integration standards are adopted consistently across projects and operating environments. Engineering quality, delivery consistency, and platform scalability improve through defined standards and governance controls.