Industry 4.0 Full‑Stack Machine Connectivity & Analytics Engineer

Confidential; Lafayette (IN); On-site; USD 80,000 - 120,000; Full time

Job summary

Confidential is seeking a Full‑Stack Machine Connectivity & Analytics Engineer in Lafayette, IN. The ideal candidate will focus on connecting manufacturing equipment to collect data, create dashboards, and provide actionable insights.

With over 7-10 years of experience, you will bridge OT/IT, ensuring insights drive operational decisions. The expected responsibilities include data analysis, developing reports, and supporting plant stakeholders for improved performance.

Qualifications

• 7-10 years of relevant experience in a contractor role.
• Hands-on experience with manufacturing equipment data and IIoT sources.
• Familiarity with continuous improvement and sustainment models.

Responsibilities

• Support connections to manufacturing equipment and secure data collection.
• Develop dashboards and analytics for operational insights.
• Ensure data insights translate into actionable decisions.

Skills

Connecting manufacturing equipment data

Data analysis and visualization

Transforming raw data into actionable insights

Working in OT, IT and engineering

Operating independently in contract roles

Tools

Power BI

CSV exports

Job description

Role

Industry 4.0 Full‑Stack Machine Connectivity & Analytics Engineer

Location

Lafayette-IN (In plant, No WFH)

Expected year of Exp

7-10 year

Role Summary

Required Full‑stack Industry 4.0 contractor to connect manufacturing equipment, collect and structure machine and process data, and convert that data into actionable, decision‑ready insights.

This role bridges OT/IT connectivity, data engineering, and manufacturing analytics, partnering closely with manufacturing engineering, operations, controls/IT‑OT, and transformation teams. The contractor will focus on rapid enablement, practical solutions, and creating repeatable patterns that scale across assets and facilities.

Key Responsibilities

Machine Connectivity & Data Acquisition

• 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.
• Document data definitions, assumptions, and limitations to enable sustainment and scale.

Analytics, Reporting & Insights

• 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.

Operational Integration & Actionability

• 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.
• Exposure to reliability, asset performance, predictive maintenance, or process monitoring use cases.

Familiarity with structured deployment playbooks, continuous‑improvement cadences, and sustainment models