How to apply for Sr. Data Engineer

About Re:Build Manufacturing

Re:Build Manufacturing works to bring next-generation products to scale in America. It operates an advanced, end-to-end manufacturing platform serving aerospace and defense, electrification, medical, energy and environment, and robotics and automation. The company says its culture is guided by 16 principles called The Re:Build Way.

About the role

This data engineer role designs and implements scalable technical solutions and supports the broader engineering team. You participate in software architecture and system build discussions, write clean, maintainable code, and use AI-supported tools to improve velocity and quality. You debug complex issues, optimize system performance, and make trade-offs between speed and long-term maintainability.

A typical day

A typical day likely includes writing and reviewing code, joining architecture or system build discussions, and debugging data or system issues. You may also test AI-supported tools to improve velocity and quality. Ask the hiring team how the data engineering group is organized and which manufacturing verticals it supports first.

Who Re:Build Manufacturing is looking for

• Has experience designing and implementing scalable data or software solutions, not just maintaining existing pipelines.
• Can write clean, maintainable code and debug complex production issues.
• Is comfortable participating in software architecture and system build discussions with engineers.
• Uses or is open to AI-supported development tools to improve speed and quality, and can make sound trade-offs between speed and long-term maintainability.

Tips for this application

• Read The Re:Build Way principles before writing anything. Reference one or two principles in your materials if they match how you work.
• Tailor your resume to show data engineering work that supported physical products or manufacturing operations, such as quality, supply chain, or production data.
• Show examples of trade-offs you made between speed and long-term maintainability. This is called out in the job description.
• Include specific AI-supported tools you have used in development and how they improved your velocity or code quality.
• Apply for the US remote role only if you can work from the US. The posting says Remote (US), so confirm your location is stated clearly in your application.

What to cover in your cover letter

['Concrete examples of designing and implementing scalable technical solutions, with outcomes.', 'A time you debugged a complex issue and optimized system performance.', 'How you use AI-supported tools in your engineering workflow and what changed as a result.', "Why Re:Build's goal of revitalizing U.S. manufacturing fits your background in data engineering."]

Research before applying

• Read The Re:Build Way principles and note which ones relate to engineering or data work.
• Look up Re:Build's verticals (aerospace and defense, electrification, medical, energy and environment, robotics and automation) and identify where data engineering matters most.
• Check Re:Build's operating companies and how the end-to-end manufacturing platform is structured.
• Find any public information about Re:Build's data stack or engineering blog. If none exists, prepare questions about the tech stack for the interview.

Likely interview topics

Based on the job description, expect questions about:

• Walk through a scalable data or software system you designed and implemented. What trade-offs did you make?
• Describe a complex production issue you debugged. How did you find the cause and fix it?
• How do you use AI-supported tools in your development process, and where do you not use them?
• How do you decide between shipping fast and building for long-term maintainability?
• How would you support a manufacturing platform that spans aerospace and defense, electrification, medical, energy, and robotics?

Common mistakes to avoid

• Sending a generic data engineer resume that does not mention manufacturing, industrial data, or physical products.
• Claiming AI tool experience without a specific example of how it improved your work.
• Ignoring the maintainability side of the role and only talking about speed or shipping fast.