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Flextronics International

Global contract manufacturer offering design engineering

Manufacturing Data Engineer - Medical Devices

Data EngineeringConfirmed live in the last 24 hours
Full-timeBuffalo Grove, IL, USA

About the Job

Requirements

  • A bachelor's degree in Engineering, Computer Science, Data Engineering, Information Systems, or a closely related technical field, or equivalent relevant experience.
  • At least 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: programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA), historians, manufacturing execution systems (MES), computerized maintenance management systems (CMMS), or manufacturing quality systems.
  • Experience developing and troubleshooting data pipelines, data models, application programming interfaces (APIs), or event-based integrations.
  • Ability to investigate discrepancies directly with manufacturing teams and translate complex data into practical, measurable shop-floor actions.
  • Analytical problem-solving, documentation, communication, and cross-functional collaboration skills.

Responsibilities

  • Design, implement, and support reliable data pipelines integrating PLCs, SCADA, historians, MES, CMMS, quality systems, and other approved manufacturing data sources.
  • Create consistent definitions for assets, equipment, lines, products, shifts, batches/lots, events, downtime, defects, and production states 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.
  • 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.

Desired Qualifications

  • Experience in medical-device or other regulated manufacturing environments and familiarity with ISO 13485 and 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.

About the Company

Flextronics International

Global contract manufacturer offering design engineering

Flex operates as a global manufacturing partner offering design, engineering, and supply chain solutions across multiple industries. It began in 1969 with hand-soldering circuit boards, pivoted to contract manufacturing in 1980, and expanded internationally, including establishing operations in Singapore. It went public on NASDAQ in 1994 and grew to serve major clients such as Microsoft. In 2015, it rebranded from Flextronics to Flex to reflect a broader services scope beyond electronics. The company also acquired Nextracker (solar trackers) in 2015 and later spun it off via IPO in 2023, illustrating its capability to manage verticals from product design to large-scale manufacturing and end-to-end supply chain management. Overall, Flex differentiates itself through its global scale, end-to-end services (from design and engineering to manufacturing and supply chain), and its ability to serve a diverse set of industries with integrated manufacturing solutions.

Company size
10,001+
Company stage
IPO
Headquarters
Singapore, Singapore
Founded
1969

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