PLM Data Analyst - PV W2

Job Title and Location PLM Data Analyst – Dearborn, Michigan (4 days on site, 1 day remote) Ideal Candidate Profile Candidate should enjoy cleaning dirty data, building smart rules, and automating repetitive data tasks. Must understand PLM fundamentals and use Python for data manipulation. Role Overview Ford IT is undergoing a massive enterprise modernization effort to migrate engineering data from legacy systems to a modern, unified platform. The PLM Data Automation & Migration Engineer will focus entirely on data correction, quality, and migration automation, rather than writing core application code. Key Responsibilities 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. Build and manage intermediate data layers and staging databases (e.g., MongoDB, SQL) to transform legacy data structures into clean unified models. Work closely with Ford’s internal Java development team to translate data cleanup rules and mapping logic into functional requirements for the migration utility pipeline. Maintain high data integrity for complex engineering structures, including Bills of Materials, Item Revisions, and associated CAD datasets. Required Skills & Qualifications 4+ years of hands‑on experience in PLM data engineering, data profiling, or data migration environments. Strong foundational knowledge of PLM principles (e.g., Teamcenter, Windchill, Enovia) and deep understanding of CAD structures, engineering changes, and BOM schemas. Proficient in Python and data analysis libraries (Pandas, NumPy, Scikit‑learn) to write custom data cleansing and automated matching scripts. Hands‑on experience querying and structuring data within staging layers or databases (MongoDB, PostgreSQL, SQL Server). Proven track record of handling complex data edge cases, resolving structure gaps, and migrating data from legacy to modernized framework. Prior experience with Teamcenter, 3DEXPERIENCE, ENOVIA, or XPDM data architectures. Exposure to basic AI/ML automation or LLM‑driven data parsing pipelines. #J-18808-Ljbffr