Digital Manufacturing Engineer - Manufacturing Data & Automation

Fairbanks Morse Defense•Walpole, MA

About The Position

The Digital Manufacturing Engineer – Manufacturing Data & Automation develops and implements data-driven solutions across machining, assembly, inspection, and test operations. The role connects shop-floor equipment and manufacturing systems, automates production and process data collection, and turns that information into actionable manufacturing intelligence. The engineer works cross-functionally with Manufacturing Engineering, Operations, Quality, Maintenance, and IT/OT to improve throughput, equipment utilization, labor productivity, quality, process capability, and overall manufacturing performance. A central objective is to establish an integrated manufacturing data environment connecting CNC machine data, Renishaw probing results, assembly performance, quality information, test results, and ERP/MES data. The position combines manufacturing-process knowledge, equipment connectivity, data engineering, analytics, and continuous improvement. Success is measured through improved performance visibility, reduced manual data collection, increased equipment and labor productivity, earlier identification of process variation, better traceability, and measurable improvements in quality, cost, delivery, and throughput.

Requirements

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 or 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.

Nice To Haves

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.

Responsibilities

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 technologies.
Develop scalable approaches to collecting, storing, structuring, and analyzing manufacturing data, and integrate shop-floor data with ERP, MES, PLM, QMS, SQL databases, historians, and other enterprise systems.
Establish common manufacturing-data definitions, naming conventions, data structures, and governance standards, and work with IT/OT to maintain secure, reliable connectivity.
Collect and analyze CNC data, including machine state, cycle time, alarms, downtime, spindle utilization, program status, tool information, and other process variables.
Develop or support Renishaw on-machine probing applications for in-process measurement, part verification, work-offset management, and process control.
Capture and analyze 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, and support closed-loop manufacturing strategies using measurement results to drive adjustments.
Partner with CNC programmers, machinists, Manufacturing Engineering, and Quality Engineering to improve machining performance using manufacturing data.
Capture and analyze assembly-production information, including labor hours, standard versus actual hours, takt and cycle time, station performance, throughput, downtime, delays, rework, defects, and first-pass yield.
Integrate data 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 schedules to improve traceability.
Develop analytics to identify assembly bottlenecks, wait and queue time, recurring delays, labor-efficiency losses, and capacity constraints.
Analyze standard-versus-actual labor performance to identify opportunities for routing, process, training, tooling, and workstation improvements.
Combine production, quality, and test information to identify where defects and rework originate.
Develop and maintain manufacturing dashboards using Power BI or similar business-intelligence platforms.
Automate reporting for manufacturing metrics such as OEE, utilization, standard versus actual hours, labor productivity, schedule attainment, cycle time, throughput, downtime, first-pass yield, scrap, rework, process capability, and quality trends.
Develop drill-down dashboards from plant-level KPIs to departments, work centers, machines, operations, and products; automate manual reports and create exception-based reporting.
Partner with Operations and Manufacturing Engineering to translate data into measurable process improvements and support Lean and continuous-improvement initiatives.
Use analytics to identify constraints, capacity opportunities, recurring downtime, process variation, quality issues, and productivity losses, and evaluate improvement-project performance before and after changes.
Support predictive and condition-based approaches for tooling, equipment, and processes, and identify opportunities for advanced analytics, machine learning, or predictive models where they provide measurable value.