Digital Manufacturing Engineer - Manufacturing Data & Automation

Position Title: Digital Manufacturing Engineer – Manufacturing Data & Automation Position Summary:The Digital Manufacturing Engineer – Manufacturing Data & Automation is responsible for developing and implementing data-driven manufacturing solutions across machining, assembly, inspection, and test operations. This position will connect shop-floor equipment and manufacturing systems, automate the collection of production and process data, and transform that information into actionable manufacturing intelligence. The engineer will work closely with Manufacturing Engineering, Operations, Quality, Maintenance, and IT/OT to improve throughput, equipment utilization, labor productivity, quality, process capability, and overall manufacturing performance. A key focus of the position will be establishing an integrated manufacturing data environment that connects CNC machine data, Renishaw probing results, assembly performance, quality information, test results, and ERP/MES data. Essential Duties and Responsibilities: Digital Manufacturing & Data Acquisition Develop and maintain automated methods for collecting manufacturing data from CNC machine tools, assembly operations, inspection equipment, test systems, and other production assets. Establish connectivity to manufacturing equipment using technologies such as MTConnect, OPC UA, APIs, PLC interfaces, industrial gateways, MQTT, or similar communication technologies. Develop scalable methods for collecting, storing, structuring, and analyzing manufacturing data. Integrate shop-floor data with ERP, MES, PLM, QMS, SQL databases, manufacturing historians, and other enterprise systems. Establish common manufacturing-data definitions, naming conventions, data structures, and governance standards. Work with IT/OT resources to maintain secure and reliable connectivity between manufacturing equipment and business systems. CNC Machining & Renishaw Data Develop and support the collection and analysis of CNC machine data, including machine state, cycle time, alarms, downtime, spindle utilization, program status, tool information, and other process variables. Develop and/or support Renishaw on-machine probing applications used for in-process measurement, part verification, work-offset management, and process control. Capture and analyze Renishaw probing results for dimensional trending, SPC, process capability, tool-wear monitoring, process-drift detection, and continuous improvement. Develop automated methods for identifying abnormal machining conditions and process variation. Support the development of closed-loop manufacturing strategies where measurement results can be used to drive machining or process adjustments. Partner with CNC programmers, machinists, Manufacturing Engineering, and Quality Engineering to improve machining performance using manufacturing data. Assembly & Production Data Develop automated methods for capturing and analyzing assembly-production information. Collect and analyze labor hours, standard versus actual hours, takt/cycle time, station performance, throughput, downtime, delays, rework, defects, and first-pass yield. Integrate information from digital torque systems, test equipment, PLCs, operator interfaces, electronic work instructions, and other connected assembly equipment where available. Link production information to work orders, part numbers, routings, operations, serial numbers, and production schedules to improve manufacturing traceability. Develop analytics to identify assembly bottlenecks, excessive wait/queue time, recurring delays, labor-efficiency losses, and capacity constraints. Analyze standard-versus-actual labor performance to identify opportunities for routing improvements, process changes, training, tooling, and workstation improvements. Combine production, quality, and test information to identify where defects and rework originate within the manufacturing process. Manufacturing Analytics & Visualization Develop and maintain manufacturing dashboards using Power BI or similar business-intelligence platforms. Establish automated reporting for key manufacturing metrics including: OEE Machine utilization Standard versus actual hours Labor productivity Schedule attainment Cycle time Throughput Downtime First-pass yield Scrap and rework Process capability Quality trends Develop drill-down capability allowing manufacturing leadership to move from plant-level KPIs to individual departments, work centers, machines, operations, and products. Automate recurring manufacturing reports currently dependent on manual data collection and manipulation. Develop exception-based reporting that identifies abnormal conditions requiring Operations or Engineering attention. Continuous Improvement & Manufacturing Intelligence Partner with Operations and Manufacturing Engineering to translate manufacturing data into measurable process improvements. Use data analytics to identify manufacturing constraints, capacity opportunities, recurring downtime, process variation, quality issues, and productivity losses. Support Lean and continuous-improvement initiatives with objective manufacturing data. Develop analytical methods to evaluate before-and-after performance of manufacturing improvement projects. Support predictive and condition-based approaches for tooling, equipment, and manufacturing processes. Identify opportunities to apply advanced analytics, machine learning, or predictive models where they provide measurable manufacturing value. Required Qualifications: Bachelor’s degree in Manufacturing Engineering, Mechanical Engineering, Industrial Engineering, Automation/Controls Engineering, Computer Engineering, or a related technical discipline; equivalent relevant manufacturing experience may be considered. 3–7 years of experience in manufacturing engineering, digital manufacturing, manufacturing automation, industrial data systems, or a related manufacturing discipline. Demonstrated understanding of discrete manufacturing operations, including machining and/or assembly. Experience collecting, analyzing, and interpreting manufacturing data. Understanding of CNC manufacturing processes and CNC control architecture. Experience with databases and data manipulation, preferably SQL. Experience with Python or another programming/scripting language used for data acquisition, automation, or analysis. Experience developing manufacturing dashboards using Power BI or a comparable visualization platform. Working knowledge of manufacturing KPIs, including OEE, utilization, cycle time, throughput, downtime, scrap, rework, first-pass yield, and labor performance. Demonstrated ability to work cross-functionally with Manufacturing Engineering, Operations, Quality, Maintenance, and IT/OT organizations. Preferred Qualifications: Experience with Renishaw probing and on-machine measurement, including probing cycles, dimensional verification, work-offset management, and extraction of measurement results. Experience with CNC controls such as Siemens SINUMERIK, FANUC, or similar platforms. Experience integrating manufacturing equipment with MES, ERP, PLM, QMS, or manufacturing historian systems. Experience with discrete assembly operations and associated production-data systems. Experience capturing data from digital torque/fastening systems, test equipment, inspection systems, and electronic work instructions. Experience with SPC, process capability, dimensional trending, and automated quality-data analysis. Advanced Power BI experience, including data modeling and DAX. Experience developing automated data pipelines between manufacturing equipment, databases, and visualization platforms. Experience in aerospace, defense, naval, or another highly regulated manufacturing environment. Key Competencies:The successful candidate should be equally comfortable on the manufacturing floor and working with manufacturing data. This position requires an individual who can understand what is happening at a CNC machine or assembly workstation, determine what information is valuable, establish a method to capture that information, and convert the resulting data into tools that Manufacturing and Operations can use to make better decisions. The ideal candidate combines manufacturing-process knowledge, equipment connectivity, data engineering, analytics, and continuous improvement rather than specializing exclusively in CNC programming or traditional IT/data analytics. Role Objective:Build a connected manufacturing environment that creates a reliable flow of information:Machining + Renishaw Probing + Assembly + Inspection + Test + Quality + ERP/MES → Manufacturing Data Platform → Analytics/Power BI → Operational Action → Process Improvement Success in this position will be measured by improved visibility of manufacturing performance, reduction of manual data collection, increased equipment and labor productivity, earlier identification of process variation, improved manufacturing traceability, and measurable improvements in quality, cost, delivery, and throughput.