Manufacturing Analytics Engineer

Prysmian

14h

About The Position

Prysmian is the world leader in the energy and telecom cable systems industry. Each year Prysmian manufacturers thousands of miles of underground and submarine cables and systems for power transmission and distribution, as well as medium low voltage cables for the construction and infrastructure sectors. We also produce a comprehensive range of optical fibers, copper cables and connectivity for voice, video, and data transmission for the telecommunication sector. We are 30,000 employees, across 50+ countries. Everyone at Prysmian has the potential to make their mark; because whatever you do, wherever you are based, you will be part of a company that is helping transform the world around us. Make Your Mark at Prysmian! Role Purpose The Manufacturing Analytics 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.

Requirements

5+ years in a manufacturing analytics role.
Bachelor’s degree in computer science, data science, or engineering
Experience with AVEVA, Ignition, PLC tags strongly preferred

Responsibilities

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.
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.