Industry 4.0 Engineer

Key Responsibilities

• Enable and support connections to manufacturing equipment, sensors, and digital sources in collaboration with controls and IT/OT teams.
• Support secure and reliable data collection from machines and systems used in manufacturing operations.
• Validate data availability, frequency, signal quality, and basic health of connected assets.
• Structure, normalize, and prepare machine and operational data so it is usable for analytics and reporting.
• Implement pragmatic data pipelines that support near-real-time and historical analysis (tool-agnostic, fit-for-purpose).
• Document data definitions, assumptions, and limitations to enable sustainment and scale.
• Develop dashboards, reports, and analytical views that translate raw data into clear operational insights and recommended actions.
• Support reporting workflows that may include structured exports (e.g., CSV) and Power BI-style dashboards depending on maturity and use case.
• Identify trends, exceptions, thresholds, and opportunities related to safety, quality, throughput, utilization, or reliability.
• Support or lead regular reviews of data insights with plant stakeholders to ensure insights turn into actions with owners and follow-up.
• Partner with manufacturing engineering, operations, EHS, quality, and IT/OT to ensure analytics align with real shop-floor decisions.
• Contribute to development of repeatable deployment patterns and best practices that can be reused across sites.

Required Skills & Experience

• Hands-on experience connecting or working with manufacturing equipment data, telemetry, or IIoT sources.
• Strong ability to transform raw machine/process data into actionable insights, not just dashboards.
• Experience with data analysis and visualization tools used in manufacturing or industrial contexts.
• Comfort working across OT, IT, engineering, and operations in a plant environment.
• Proven ability to operate independently in a contract role with minimal direction.

Preferred / Nice-to-Have Experience

• Exposure to reliability, asset performance, predictive maintenance, or process monitoring use cases.
• Familiarity with structured deployment playbooks, continuous-improvement cadences, and sustainment models.