PLM Data Automation & Migration Engineer
Job Location:
Job Location:
Dearborn, MI - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
4 hours ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Our Client is undergoing a massive enterprise modernization effort to migrate engineering data from legacy systems to a modern unified platform. We are seeking a PLM Data Automation & Migration Engineer to join our team. The main challenge of this role is not writing core application code-we have a dedicated team of software developers for that. Instead this position is focused entirely on data correction data quality and migration automation.
Currently our data validation and cleanup processes are manual. You will be responsible for leveraging Python and data-driven rule engines to build an automated framework that detects profiles and cleanses massive engineering data structures (BOMs CAD metadata and part revisions) before they are loaded into the target platform.
Currently our data validation and cleanup processes are manual. You will be responsible for leveraging Python and data-driven rule engines to build an automated framework that detects profiles and cleanses massive engineering data structures (BOMs CAD metadata and part revisions) before they are loaded into the target platform.
Key Responsibilities
Data Quality Automation: Move the team from manual data profiling to automation. Design and implement Python or rule-based scripts to scan detect and automatically resolve metadata discrepancies attribute mismatches and structure gaps.
Data Mapping & Transformation: Build and manage the intermediate data layers and staging databases (e.g. MongoDB SQL) used to transform legacy data structures into clean unified models.
Cross-Functional Integration: Work closely with Fords internal team of Java developers translating data cleanup rules and mapping logic into functional requirements for the migration utility pipeline.
Engineering Data Stewardship: Maintain high data integrity for complex engineering structures including Bills of Materials (BOMs) Item Revisions and associated CAD datasets.
Data Quality Automation: Move the team from manual data profiling to automation. Design and implement Python or rule-based scripts to scan detect and automatically resolve metadata discrepancies attribute mismatches and structure gaps.
Data Mapping & Transformation: Build and manage the intermediate data layers and staging databases (e.g. MongoDB SQL) used to transform legacy data structures into clean unified models.
Cross-Functional Integration: Work closely with Fords internal team of Java developers translating data cleanup rules and mapping logic into functional requirements for the migration utility pipeline.
Engineering Data Stewardship: Maintain high data integrity for complex engineering structures including Bills of Materials (BOMs) Item Revisions and associated CAD datasets.
Required Skills & Qualifications
Experience: 4 years of hands-on experience in PLM data engineering data profiling or data migration environments.
PLM Fundamentals: Strong foundational knowledge of Product Lifecycle Management (PLM) principles (e.g. Teamcenter Windchill Enovia or similar) with a deep understanding of CAD structures engineering changes and BOM schemas.
Data Tooling: Proficient in Python and standard data analysis libraries (Pandas NumPy Scikit-learn) to write custom data cleansing and automated matching scripts.
Staging Databases: Hands-on experience querying and structuring data within staging layers or databases (such as MongoDB PostgreSQL or SQL Server).
Problem-Solving Background: Proven track record of handling complex data edge cases resolving structure gaps and migrating data from a legacy state to a modernized framework.
Experience: 4 years of hands-on experience in PLM data engineering data profiling or data migration environments.
PLM Fundamentals: Strong foundational knowledge of Product Lifecycle Management (PLM) principles (e.g. Teamcenter Windchill Enovia or similar) with a deep understanding of CAD structures engineering changes and BOM schemas.
Data Tooling: Proficient in Python and standard data analysis libraries (Pandas NumPy Scikit-learn) to write custom data cleansing and automated matching scripts.
Staging Databases: Hands-on experience querying and structuring data within staging layers or databases (such as MongoDB PostgreSQL or SQL Server).
Problem-Solving Background: Proven track record of handling complex data edge cases resolving structure gaps and migrating data from a legacy state to a modernized framework.
Preferred Skills (Nice to Have)
Prior experience with Teamcenter 3DEXPERIENCE or ENOVIA / XPDM data architectures.
Exposure to basic AI/ML automation or LLM-driven data parsing pipelines.
Prior experience with Teamcenter 3DEXPERIENCE or ENOVIA / XPDM data architectures.
Exposure to basic AI/ML automation or LLM-driven data parsing pipelines.
