Manufacturing Data Engineer - Medical Devices

Flex•Buffalo Grove, IL

1d•$99,900 - $137,400•Onsite

About The Position

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 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. This newly created, full-time position reports to the Senior Manager of Digital Manufacturing. It is based at Flex's manufacturing site in Buffalo Grove, Illinois, with occasional work at the Libertyville, Illinois site. There is no travel requirement. The position owns technical execution and data-to-action enablement. Manufacturing process owners retain accountability for operational decisions and completing assigned corrective actions; enterprise architecture, OT controls, and overall digital manufacturing strategy remain governed by designated leaders.

Requirements

Bachelor’s degree in Engineering, Computer Science, Data Engineering, Information Systems, or a closely related technical field, or equivalent relevant experience.
6+ years of relevant experience in manufacturing data engineering, industrial systems integration, manufacturing analytics, or industrial automation.
Hands-on proficiency with SQL and Python for data transformation, analysis, validation, and integration.
Working knowledge of industrial equipment data and at least some of the following: PLCs, SCADA, historians, MES, CMMS, or manufacturing quality systems.
Experience developing and troubleshooting data pipelines, data models, APIs, or event-based integrations.
Ability to investigate discrepancies directly with manufacturing teams and translate complex data into practical, measurable shop-floor actions.
Strong analytical problem-solving, documentation, communication, and cross-functional collaboration skills.

Nice To Haves

Experience in medical-device or other regulated manufacturing environments and familiarity with ISO 13485 / FDA quality-system expectations.
Familiarity with MQTT, unified namespace patterns, OPC UA, ISA-95, time-series data, and industrial data historians.
Experience with Power BI, semantic models, cloud or hybrid data platforms, streaming tools, and CI/CD or automated testing.
Exposure to OEE, scrap and yield analytics, maintenance reliability, statistical process control, machine learning, or predictive quality.
Experience integrating analytics outputs with CMMS, MES, ticketing, or workflow systems to create and track corrective actions.

Responsibilities

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, and 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.
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.
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, ensuring recommendations are explainable and validated before operational use.
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 without bypassing 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.