Manufacturing Systems & Data Analyst

Description

Position: In House M- F

Hours: 7:30 - 4:30

Salary: 65,000 - $85,000

The nature of this role

This is a floor role that produces data, not an office role that produces reports.

The department's constraint is not a shortage of dashboards — it is that the process does not yet generate information anyone can rely on. The deliverable of this position is working measurement: capture designed at the point and moment the work happens, accurate enough to make decisions on, and durable enough to keep running without the post-holder standing over it. Analysis follows measurement; it does not substitute for it.

Position Summary

The Manufacturing Systems & Data Analyst gives the Assembly department numbers it can trust. The role designs and installs practical measurement on the shop floor, owns the accuracy of what is captured, and converts it into the small set of measures the department uses to run itself and to make decisions.

Hours are recorded against jobs and the detail exists in the system, but it is reported in aggregate: the capability to extract it, break it down, reconcile it and establish whether it is accurate does not exist in the department's structure today. Figures therefore cannot be traced to a cause or defended when they conflict with what is visible on the floor. Staffing is planned by section count rather than by the work a job actually contains.

Wherever the department needs to see something it currently cannot — where time is going, what capacity is genuinely available, what a job costs against what was quoted, whether a change actually improved anything — or needs to confirm that something it already reports is true, this role builds the means to do it, proves the figure is sound, and keeps it sound once built.

Essential Duties and Responsibilities

• Design and install shop-floor data capture that people will actually complete — developed with the teams doing the work rather than issued to them — and own its accuracy. Capture that takes more than a minute does not get done, and partial data is more dangerous than none because it appears complete.
• Own data integrity as a deliverable, including capture rate: reconcile what is recorded against what actually occurred, and do not publish analysis built on data whose completeness is unknown.
• Validate the measures the department already reports — test them against independent evidence, resolve discrepancies rather than restating them, and raise the level of detail at which data is captured so that a figure can be traced to the job, the operation and the cause.
• Design and run targeted measurement studies to answer specific operational questions the department cannot currently answer, reporting the method alongside the result so the figure can be trusted and repeated.
• Develop the department's work-content unit — a measure of the work a job actually contains, validated against actual hours on completed jobs — so that load and capacity can be compared in the same terms.
• Build and maintain the department scorecard across safety, quality, delivery and cost, drawn from source data with no manual re-keying.
• Report labour and material variance to estimate on every closed job, and feed systematic findings back to estimating and quoting.
• Develop labour-hours-based staffing models accounting for leave, absence, turnover and training time, replacing staffing planned by section count.
• Support capacity planning with the Planner and Manufacturing Engineering as time standards mature.
• Build self-serve reporting so that managers pull their own numbers, and the analyst's time goes to measurement and analysis rather than to re-reporting.
• Hand the running of capture routines to line leadership — they should sit inside supervisors' and leads' normal work, so the measurement survives any change of post-holder.

Success Measures

• Capture rate — what is recorded against what actually occurred — for each measure the department relies on
• Percentage of active jobs with complete hour capture — target 100%
• Scorecard published on cadence with zero manual re-keying
• Variance-to-estimate reporting cycle time after job close
• Adoption: percentage of managers self-serving their own metrics
• Capture routines running unaided by line leadership within twelve months

Required Qualifications

• Two to five years in operations analysis, industrial engineering support or manufacturing data analysis
• Demonstrated experience standing up shop-floor data collection — and a working understanding of what data people will and will not enter
• Comfortable spending a substantial share of the working week on the production floor
• Able to define a measurement method, not only to analyse data someone else has collected
• Advanced spreadsheet skills; experience with Power BI, Tableau or similar
• Able to explain findings simply to non-analytical audiences

Preferred Qualifications

• ERP/MES data extraction experience
• Exposure to labour standards, time study, or variance analysis in job-shop or engineered-to-order costing
• SQL or basic scripting for data automation
• Experience in an environment where measurement was being introduced rather than maintained

Working Conditions

Manufacturing floor environment with regular exposure to industrial equipment; PPE required in designated areas. The role splits time between the production floor and office work.