PLM Data Analyst - PV W2

Job Description -

Title: PLM Data Analyst

Location: 4 days on site 1 Day remote In Dearborn, Michigan

LinkedIn, Visa and Dl copy

Sourcing Blueprint: What to Prioritize

1. The Ideal Candidate Profile

You are looking for a Data Quality Engineer or a PLM Migration Specialist from the manufacturing, automotive, or aerospace sectors. This is someone who enjoys cleaning up "dirty data" and building smart rules to automate repetitive data tasks.

2. Core Requirements to Screen For (Ranked by Importance)

• PLM Fundamentals (Must Have): They must understand how product engineering data is structured. They need to know what a Bill of Materials (BOM) is, what an Item Revision is, and how CAD metadata connects.
• The Python Scripting Stack: Since Java is handled by Ford's internal team, this candidate needs Python (specifically libraries like Pandas, NumPy, or Scikit-learn). They will use this to manipulate giant data sheets, find discrepancies, and build automated matching rules.
• The "Manual to Automated" Mindset: They need to be someone who looks at a team doing manual Excel lookups or manual data entries and says, "I can write a script to automate 90% of this."

3. What to Avoid

• ? Pure Java/C++ Application Developers (they will be bored and want to write code, not clean data).
• ? High-level PLM Project Managers (they aren't hands-on enough to touch the staging databases).

4. Target Job Titles for LinkedIn

• PLM Data Analyst
• PLM Migration Specialist
• PLM Data Quality Engineer
• Manufacturing Data Engineer
• PLM Functional Consultant

?? The Realistic Job Description

This streamlined version removes the confusing Java/C++ developer requirements and highlights the actual data automation, data profiling, and cleanup scope requested by the manager.

PLM Data Automation & Migration Engineer Ford IT

Role Overview

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 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.

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 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.

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.
• Prior experience with Teamcenter, 3DEXPERIENCE, or ENOVIA / XPDM data architectures.
• Exposure to basic AI/ML automation or LLM-driven data parsing pipelines.