ALTEN Technology USA
Dimensional Engineer
About this role
ALTEN Technology USA seeks a Dimensional Engineer in Dundee, Michigan to lead dimensional improvements on vehicle geometry through analysis and corrective actions. You'll analyze complex parts and sub-assemblies using measurement systems, identify root causes, and collaborate with production and quality teams to implement solutions in an automotive manufacturing environment.
What you'll do
- Lead dimensional improvements on vehicle geometry through part, tooling, or design actions
- Analyze complex parts and sub-assemblies using measurement systems (CMMs, laser trackers, vision stations)
- Perform data analysis to identify top issues and root causes from multiple sources
- Work with production and quality personnel to implement and validate corrective actions
- Monitor trend charts and drive variation reduction of critical product characteristics
- Communicate with internal and external suppliers to resolve dimensional issues
What they're looking for
- GD&T (Geometric Dimensioning and Tolerancing)
- NX CAD system and TeamCenter
- Measurement data interpretation
- Statistical analysis and process control
- Excel and Microsoft Office proficiency
- Cross-functional communication
- Problem-solving and root cause analysis
- Data analysis and trending
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ALTEN Technology USA
ALTEN Technology USA provides engineering validation, testing, and technical support services for automotive systems including connected vehicles, propulsion controls, infotainment, battery systems, and power electronics. The company is hiring validation engineers, test engineers, design engineers, and issue resolution specialists to develop test plans, verify compliance, investigate technical issues, and support product development across automotive hardware and software platforms.
View all jobs at ALTEN Technology USALikely interview questions
- Describe your experience interpreting GD&T and how you've applied it to resolve dimensional issues on manufactured parts.
- Walk us through a time you analyzed measurement data from multiple sources to identify a root cause—what tools did you use and what was the outcome?