[Hiring] Industrial Data Engineer @Prysmian

Role Description

The Industrial Data Engineer will serve as the technical and analytical bridge between Operations Technology (OT) and enterprise analytics. This role owns the end-to-end flow of operational data—from historians and SCADA systems through to cloud storage and business intelligence—transforming raw plant data into trusted, actionable insights that power operational excellence and future AI initiatives across the organization.

Qualifications

• 5+ years in a manufacturing analytics role.
• Bachelor’s degree in computer science, data science, or engineering.
• Experience with AVEVA, Ignition, PLC tags required.
• Willingness to travel at least 50% also required.

Requirements

• Manage the entire analytics pipeline—from data collection at the historian layer to visualization and reporting.
• Own end-to-end data flow from OT historians (PI, Aveva, Ignition) to analytics and reporting environments.
• Design, script, and maintain ETL/ELT pipelines for historian-to-cloud data movement using Python, SQL, and AWS services.
• Develop reusable data models, APIs, and standardized data structures for use across plants and digital platforms.
• Troubleshoot and resolve data flow disruptions, historian tag failures, and dashboard refresh issues.
• Collaborate with plant engineers, OT, and IT teams to maintain secure, standardized, and reliable data pipelines.

Tools, Tech Stack & Standards

• OT & Historian Layer: AVEVA PI System / AF, Aveva Insight, Ignition (Vision, Perspective, Tag Historian).
• Visualization & BI: QlikSense enterprise dashboards.
• Data Transformation: SQL and Python for modeling, scripting, and automation.
• Cloud Integration: AWS services (S3, Glue, Lambda, IoT Core) for data storage, transformation, and orchestration.
• Governance: Implement and enforce data naming conventions, lineage documentation, and retention policies.
• Automation: Develop and maintain automated ETL pipelines and data orchestration workflows.

Expected Outcomes & Deliverables

• Deliver automated, validated, and standardized dashboards and reports for OEE, downtime, scrap, and energy KPIs.
• Develop APIs and data connectors to share information across MES, CMMS, and other digital platforms.
• Ensure data models and structures are AI-ready for future machine-learning applications.
• Standardize KPI definitions and data models across plants, allowing variation only where process differences require.
• Integrate analytics and insights into operational processes and decision-making workflows.
• Mentor junior analysts and plant associates, building local data literacy and self-service analytics capabilities.

Organizational Fit & Growth

• This role is positioned within the OT and Operations organization, acting as a bridge across Operations, IT, and Engineering.
• Reports to: OT and Operations organization.
• Collaborates with IT for ERP, AWS, and network integrations.
• Works with Engineering at the plant level for PLC-to-server and OT network data reliability.
• Initial focus: Stabilize and standardize data flows and dashboards across plants.
• Long-term evolution: Grow into Data Architecture and Systems Strategy leadership, defining enterprise standards for OT data.
• Future collaboration: Partner with external AI specialists when the organization moves into predictive and AI-driven initiatives.

Why Join

This position offers the opportunity to define the digital data backbone of the organization’s manufacturing network. You will build systems that not only enable operational visibility today but also form the foundation for tomorrow’s AI-driven manufacturing intelligence. The Controls Engineer will play a pivotal role in connecting plants, data, and decisions.