Manufacturing Data Engineer - Medical Devices

Flex; United States; Vor Ort; USD 95.000 - 140.000; Vollzeit

Zusammenfassung

Flex, a diversified manufacturing partner, seeks a Manufacturing Data Engineer to design and sustain trusted data foundations across multiple facilities. You will connect production equipment and systems, validate data, and translate signals into prioritized actions for Operations, Reliability, and IT/OT teams.

Based at Flex’s Buffalo Grove, IL site (with potential Libertyville, IL) you will collaborate to enable the Factory of the Future roadmap in a regulated medical-device environment,

Qualifikationen

• Bachelor's degree in a technical field or equivalent
• Experience with data integration across manufacturing systems is expected
• Familiarity with ISA-95 concepts beneficial

Aufgaben

• Design, implement, and support reliable data pipelines integrating PLCs, SCADA, historians, MES, CMMS and quality systems.
• Define data definitions for asset, equipment, line, product, batch/lot, event, downtime and production state.
• Validate data against source records and implement automated quality checks for completeness and accuracy.
• Translate data into actionable manufacturing insights and drive improvements in OEE, yield and downtime.

Kenntnisse

SQL

Python

Data pipelines

ISA-95 concepts

Ausbildung

Bachelor's degree in Engineering/CS

Tools

SCADA

MES

CMMS

Historians

Jobbeschreibung

Flex is the diversified manufacturing partner of choice that helps market-leading brands design, build and deliver innovative products that improve the world.

A career at Flex offers the opportunity to make a difference and invest in your growth in a respectful, inclusive, and collaborative environment.

Job Summary

This newly created, full-time position will report to the Senior Manager of Digital Manufacturing.

Will be based at Flex's state of the art, 24x7 manufacturing site in Buffalo Grove, IL (May sometimes work at new Libertyville, IL, site).

Travel requirement: None.

Position Summary

The Manufacturing Data Engineer develops and sustains the trusted industrial data foundation that enables actionable factory intelligence. This hands-on role connects and contextualizes information from production equipment and manufacturing systems, validates automated data for accuracy, and translates operational signals into prioritized, traceable action assignments for the teams best positioned to respond. Working closely with Operations, Manufacturing Engineering, Reliability, Maintenance, Quality, and IT/OT, the engineer closes the loop from data to decision to action to verified business impact. The role supports a scalable Factory of the Future roadmap across multiple manufacturing facilities in a regulated medical-device environment.

Key Responsibilities – Industrial Data & Context

• Design, implement, and support reliable data pipelines integrating PLCs, SCADA, historians, MES, CMMS, quality systems, and other approved manufacturing data sources.
• Create consistent asset, equipment, line, product, shift, batch/lot, event, downtime, defect, and production-state definitions so data has clear operational meaning.
• Apply industrial information models and appropriate ISA-95 concepts; support scalable event-driven architectures and unified manufacturing data patterns in partnership with IT/OT.
• Document source-to-target mappings, calculation logic, lineage, ownership, and refresh expectations for critical manufacturing metrics.

Key Responsibilities – Data Accuracy & Governance

• Validate automated data against equipment behavior, production records, operator observations, and system-of-record transactions; investigate discrepancies at the source.
• Implement automated quality checks for completeness, timeliness, duplicates, incorrect classifications, sensor faults, missing events, and anomalous values.
• Partner with process owners to establish governed definitions and trustworthy calculations for OEE, availability, performance, quality, scrap, yield, cycle time, downtime, and asset performance.
• Own issue triage and root-cause correction for assigned data products; provide monitoring, alerts, change controls, and clear documentation of data limitations.

Key Responsibilities – Actionable Manufacturing Intelligence

• Translate validated insights into specific action recommendations and task assignments, with an accountable owner, priority, supporting evidence, due date, and expected operational benefit.
• Configure and improve rules, thresholds, alerts, and workflow integrations that route exceptions to the appropriate Operations, Quality, Maintenance, Reliability, or Engineering team.
• Work with process owners to define response criteria, escalation paths, closure evidence, and feedback loops; verify whether actions resolve the underlying issue.
• Enable Pareto analysis, loss-tree reporting, and drill-down analysis to identify recurring causes of scrap, unplanned downtime, reduced throughput, and quality losses.
• Support predictive and prescriptive analytics use cases, including anomaly detection, predictive maintenance, and predictive quality, while ensuring recommendations are explainable and validated before operational use.

Key Responsibilities – Delivery & Collaboration

• Develop reusable, maintainable data models, SQL/Python transformations, APIs, and analytics-ready datasets that support dashboards and future AI applications.
• Collaborate with Digital Manufacturing leadership on use‑case prioritization, requirements, technical feasibility, deployment, and adoption across factories.
• Partner with IT and OT cybersecurity teams to meet architecture, access, network segmentation, security, backup, and support requirements; do not bypass established controls.
• Support regulated manufacturing requirements through documented testing, change management, traceability, and applicable quality-system procedures.
• Measure delivered value through improved data trust, reduced manual reconciliation, action completion, and verified improvements in scrap, OEE, yield, downtime, and operating performance.

Required Qualifications

• Bachelor's degree in Engineering, Computer Science, Data Engineering, Information Systems, or a closely related technical field, or equivalent relevant