PLM Data Analyst - PV W2

Position Details

Title:

PLM Data Automation & Migration Engineer

Location:

4 days on site, 1 day remote – Dearborn, Michigan

Ford IT 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 – that is handled by a dedicated team of software developers. 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.

• 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., Mongo
  
  DB, SQL) used to transform legacy data structures into clean unified models.
• Cross‑Functional Integration:
  Work closely with Ford's 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.

• 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, Num Py, 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 Mongo
  
  DB, Postgre
  
  SQL, 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.
• Prior experience with Teamcenter, 3
  
  DEXPERIENCE, or ENOVIA / XPDM data architectures.
• Exposure to basic AI/ML automation or LLM‑driven data parsing pipelines.