What are the responsibilities and job description for the Industry 4.0 Engineer position at Kaleidoscope Innovation?
* Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.*
Location: Lafayette, IN (on-site 5 days a week)
Years of experience required: 7-20 years
Overview
S eeking a full‑stack Industry 4.0 contractor to help accelerate our journey 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
Location: Lafayette, IN (on-site 5 days a week)
Years of experience required: 7-20 years
Overview
S eeking a full‑stack Industry 4.0 contractor to help accelerate our journey 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.
- 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.
- Connected asset and data source inventory with signal definitions and data quality notes.
- Initial analytics package (dashboards and/or reports) tied to specific, agreed‑upon operational questions.
- Documented data pipeline and reporting approach suitable for handoff or replication.
- Established cadence for insight review and action tracking with plant stakeholders.
- 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.