Senior Data Engineer - Remote Eligible

Senior Data Engineer

Our mission at Acelab is to transform how the building industry makes material decisions. We've created a comprehensive platform that connects architects with the right materials for their projects because we believe materials are fundamental to transforming inspired designs into exceptional buildings.

At the heart of our mission is the understanding that material choices shape aesthetics, performance, sustainability, and ultimately, the human experience of built spaces. We recognize that architects face overwhelming challenges in navigating hundreds of thousands of products while trying to capture and maintain their firm's collective material expertise.

Through our Material Hub platform, we aim to:

• Empower architects to make material choices that truly matter
• Provide easier access to innovative products and collective knowledge
• Elevate not just individual buildings but the entire built environment
• Create a shared language for materials that enables seamless communication between architects, manufacturers, contractors, and clients
• Preserve institutional knowledge within architecture firms
• Streamline the material selection and specification process

Our mission statement "Because Materials Matter" encapsulates our commitment to elevating material selection from a fragmented, time-consuming process to a strategic aspect of architectural excellence. By connecting the industry's deepest technical database with material decision-making workflow tools, we're working to ensure that every project benefits from better informed material decisions.

We are seeking a Senior Data Engineer to join our AI-focused data team and lead the development of scalable data processing and enrichment pipelines. You'll work at the cutting edge of AI-powered data engineering, building production-grade systems that power our Material Hub platform and enable architects to access comprehensive, up-to-date material information.

The work you'll do includes:

• Production Engineering: Transform experimental AI workflows into robust, automated production systems with comprehensive monitoring and quality assurance
• System Architecture: Design scalable data processing pipelines, create reusable modular components, and establish engineering standards for team collaboration
• Data Quality & Automation: Build evaluation frameworks to monitor pipeline quality, implement automated error handling, and reduce manual intervention requirements

Technical Leadership: Mentor team members, conduct thorough code reviews, and set best practices for AI system design