Enovia Data Migration & CAD Integration Engineer (Adaplet)

Role Overview

Own the migration of CAD/CAE data migration into ENOVIA using Adaplet and/ or MQL / TCL scripts on the 3DX DEC platform and configure Adaplet-based data migration. Map legacy structures to the target model and ensure integrity of migrated design data.

Key Responsibilities

• Design and execute CAD/CAE data migration into ENOVIA using Adaplet connectors
• Develop MQL/TCL scripts as needed to migrate / extract data from / to Enovia
• Configure and maintain integrations for CATIA (native, backported), NX (via ITI connector) and Zuken.
• Mapping of types and relationships from legacy Enovia data model to 3DXperience Unified Product Structure
• Build extraction, transformation and load routines for large design datasets and validate results
• Reconcile migrated BOM, part and document structures; resolve data-quality issues.

Required Qualification

• 1-5 years in PLM data migration / CAD integration, ideally on ENOVIA 3DX DEC.
• Hands-on experience with Adaplet and at least one CAD connector (CATIA, NX, Zuken).
• Bachelors or masters degree in engineering, Computer application or related majors.
• Proficient and hands-on experience with Dassault 3DX Enovia 2015x version (or any legacy version of 3DXperience)
• Hands on experience on 3DXperiences Unified Product Structure (UPS/VPM) migration
• Expertise in managing, developing CAD integrations with PowerBy and native Virtual Product Management in latest 3DXperience versions.
• Azure/AWS: Familiarity with cloud storage, secure file transfer, networking and access-control concepts (Good to have)

Skills & Competencies / Tools

• Technical: ENOVIA 3DEXPERIENCE Adaplet, CATIA/NX connectors, PowerBy, 3DShape/VPM Reference, ETL and data migration, SQL Server, MQL, Tcl/Tk, data mapping, migration validation, and basic REST API knowledge
• Interpersonal: Collaboration across cross-functional teams, stakeholder communication, conflict resolution, and knowledge sharing.
• Analytical: Structured problem-solving, root-cause analysis, and data-driven decision-making.