Data Engineer – Manufacturing

🕒 Dezembro 11, 2025

🍂 Massachusetts – Remoto

🍂 Massachusetts – Remote

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🚰 Engenheiro de Dados

👻 Score fantasma 76%

O que significa: quão provável esta vaga está aberta sem contratação ativa.Como ler: 60% ou mais significa que achamos que é uma vaga fantasma. Pontuações mais baixas preocupam menos.Como pontuamos: tempo em aberto, republicações, vagas abertas vs tamanho do time, rapidez para fechar vagas, e se o salário está divulgado.Observação: é uma estimativa de dados públicos, não um julgamento da empresa. Vagas reais podem ter pontuação alta, então use como uma dica, não como um fato.

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Foundation EGI

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2023

🏭 Manufatura

💼 Consultoria

📦 Logística

Manufacturing • Consulting • Logistics

A Foundation EGI é uma plataforma orientada por IA que automatiza a geração de documentação de engenharia a partir de dados CAD 3D. A empresa converte montagens em catálogos de peças 2D/3D, desenhos técnicos de GD&T, sequências otimizadas de montagem e manuais de operação/serviço multilíngues, visando reduzir a elaboração manual de projetos e acelerar o desenvolvimento de produtos para equipes de manufatura e engenharia. A Foundation EGI se posiciona como uma solução SaaS empresarial que integra aprendizado de máquina e design computacional em fluxos de trabalho industriais para melhorar a precisão, a rastreabilidade e o tempo de produção.

Descrição

• 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.

🎯 Requisitos

• 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.