Data Engineer – Manufacturing

🕒 December 11, 2025

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 76%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Foundation EGI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

🏭 Manufacturing

💼 Consulting

📦 Logistics

Manufacturing • Consulting • Logistics

Foundation EGI is an AI-driven platform that automates the generation of engineering documentation from 3D CAD data. The company converts assemblies into 2D/3D parts catalogs, GD&T technical drawings, optimized assembly sequences, and multilingual operator/service manuals, aiming to reduce manual drafting and speed product development for manufacturing and engineering teams. Foundation EGI positions itself as an enterprise SaaS solution that brings machine learning and computational design into industrial workflows to improve accuracy, traceability, and time-to-production.

📋 Description

• Ingest, clean, transform, and structure customer and internally generated engineering data for AI training and inference. • Design and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systems. • Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoning. • Apply engineering judgment to define and assess output quality across datasets. • Continuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets. • Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation. • Provide detailed feedback on tool usability, workflow efficiency, and automation opportunities. • Help develop scalable, repeatable data processes that improve throughput and data consistency. • Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data. • Influence model behavior by supplying representative engineering examples and ground-truth mechanical designs. • Partner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specifications. • Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts. • Generate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training data. • Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices. • Embed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent. • Work with customers to understand their data sources, schemas, formats, and quality expectations. • Guide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelines. • Support delivery timelines by communicating progress clearly and surfacing risks or issues early. • Review and work with external contractors, ensuring high-quality output and adherence to SOPs.

🎯 Requirements

• Strong domain expertise in mechanical engineering, manufacturing design, or industrial workflows. • Hands-on experience with CAD tools such as SolidWorks, CATIA, Siemens NX, or Creo. • Familiarity with annotation tools and illustration software (e.g., Creo Illustrate, Adobe Illustrator, Arbortext). • Ability to interpret complex mechanical assemblies, technical drawings, GD&T, and engineering documentation. • Experience creating artifacts like exploded views, work-step sequences, repair manuals, or manufacturing instructions. • Strong problem-solving skills and the ability to translate domain workflows into structured data requirements. • Excellent communication and cross-functional collaboration skills.