Global Floor Plan & Space Data Lead

Socket.dev; Arlington (VA); On-site; USD 130,000 - 175,000; Full time

Job summary

Amazon's Global Real Estate and Facilities (GREF) team seeks a Production Design Lead to own end-to-end floor plan and space data production for a 60MM+ SF global portfolio. You will oversee a vendor team, drive CAD-to-Revit conversion, and build a global intake and QC process to ensure high-quality record plans and data for occupancy planning and capital decisions.

You will develop staffing models based on pipeline data, identify automation opportunities, and implement scalable workflows across

Qualifications

• 6+ years of design experience and an online portfolio.
• Experience in Design/Construction using Autodesk Revit to coordinate large projects.
• Experience with CAD software (AutoCAD, REVIT, BIM 360, etc.).
• Experience building scalable programs and repeatable processes for high-volume production.
• Experience managing a pipeline of drafting work at scale (retail rollout/portfolio management).
• Familiarity with ACC/Forma, including building workflows and integrations.

Responsibilities

• Own the accuracy and timeliness of all record floor plans and space data transmitted globally.
• Own the quality and throughput of test-fit production for the design team.
• Lead the CAD-to-Revit portfolio conversion, including automation use.

Skills

Design experience

Online portfolio

CAD coordination

Revit coordination

Drafting at scale

Tools

Autodesk Revit

AutoCAD

BIM 360

Forma

ACC (Autodesk Construction Cloud)

Job description

Amazon's Global Real Estate and Facilities (GREF) team seeks a Production Design Lead to own end-to-end floor plan and space data production for a 60MM+ SF global portfolio. You will oversee a vendor team, drive CAD-to-Revit conversion, and build a global intake and QC process to ensure high-quality record plans and data for occupancy planning and capital decisions.

You will develop staffing models based on pipeline data, identify automation opportunities, and implement scalable workflows across