Digital Manufacturing Engineer - Manufacturing Data & Automation

Fairbanks Morse Defense•Walpole, MA

1d•$86,391 - $129,588

About The Position

The Digital Manufacturing Engineer – Manufacturing Data & Automation develops and implements data-driven manufacturing solutions across machining, assembly, inspection, and test operations. The position connects shop-floor equipment and manufacturing systems, automates production and process data collection, and turns that information into actionable manufacturing intelligence. Working with Manufacturing Engineering, Operations, Quality, Maintenance, and IT/OT, the engineer helps improve throughput, equipment utilization, labor productivity, quality, process capability, and overall manufacturing performance. A key focus is establishing an integrated manufacturing data environment connecting CNC machine data, Renishaw probing results, assembly performance, quality information, test results, and ERP/MES data. The successful candidate is comfortable both on the manufacturing floor and working with manufacturing data. They understand production processes and equipment, identify valuable information, establish ways to capture it, and develop tools Manufacturing and Operations can use to make better decisions. The role combines manufacturing-process knowledge, equipment connectivity, data engineering, analytics, and continuous improvement. Success is measured by improved visibility of manufacturing performance, reduced 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.

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/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, and MQTT.
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.
Develop and support 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 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 to identify abnormal machining conditions and process variation, and support closed-loop manufacturing strategies that use measurement results to drive machining or process adjustments.
Partner with CNC programmers, machinists, Manufacturing Engineering, and Quality Engineering to improve machining performance using manufacturing 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 or 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 in the manufacturing process.
Develop and maintain manufacturing dashboards using Power BI or similar business-intelligence platforms.
Establish automated reporting for 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, and quality trends.
Develop drill-down capability from plant-level KPIs to departments, work centers, machines, operations, and products.
Automate recurring manufacturing reports that depend on manual data collection and manipulation.
Develop exception-based reporting to identify abnormal conditions requiring Operations or Engineering attention.
Partner with Operations and Manufacturing Engineering to translate manufacturing data into measurable process improvements.
Use 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.