Offer summary

Qualifications:

BS/MS in Computer Science, Information Systems, or related field., 5+ years of experience in ETL development and data engineering., Proficiency in Python programming and database systems (SQL, noSQL)., Experience with pipeline orchestration tools like Prefect, infrastructure-as-code such as Terraform, and monitoring tools..

Key responsibilities:

Design, develop, and maintain data processing pipelines.

Manage data flow through various processing stages.

Collaborate with machine learning teams to improve pipeline functionality.

Ensure data quality and monitor pipeline health.

Job description

At Trunk Tools, we’re the leading AI company revolutionizing construction—the secondlargest industry on earth. We recently raised a $40M Series B led by Insight Partners, bringing our total funding to $70M from toptier investors including Redpoint and Innovation Endeavors. This new round is fueling our next phase of growth as we scale AI agents across the jobsite.

Our mission is to build the future of construction through intelligent automation. Despite being a $13+ trillion industry, construction still runs largely on analog processes—we’re changing that by embedding AI directly into field operations.

Founded by builders and technologists (Stanford, MIT), our team has delivered software used by over 140,000 field professionals, impacting millions of users and contributing to $10B+ in built projects. Many of us come from the field ourselves, giving us a deep understanding of the industry’s unique challenges.

After years of building the “brain” of construction, we’re now launching productionready AI agents—starting with intelligent document processing and Q&A, and rapidly expanding into core operational workflows. Our team has doubled in the past year, and with 65+ employees (25+ engineers), we’re scaling fast and entering a period of hypergrowth—this is a rare opportunity to join at an inflection point.

What you will do and achieve:

Design, develop and maintain robust file processing pipeline infrastructure
Orchestrate the flow of data through various stages of processing
Ensure observability and monitoring of the pipeline’s health
Integrate data from various sources including industry storage platforms, project management tools, and external APIs
Implement data quality checks and error handling mechanisms to ensure data integrity
Collaborate with the machine learning team to enhance pipeline functionality and efficiency