Drew Erickson is a seasoned heavy civil construction and infrastructure leader with more than 30 years of hands-on experience delivering billion-dollar projects, building high-performing teams, and driving business development across the Western United States. As President of Amaya Enterprises and a Board Member at Bulk Exchange, he blends market intelligence, sales leadership, and operational optimization to help contractors win work and scale profitably. Drew played a pivotal role in securing and standing up the $1.5B California High-Speed Rail CP 2-3 contract for the Dragados-Flatiron JV and has managed annual underground construction portfolios exceeding $150M. He’s known for improving project performance—achieving a 280 basis-point lift at Blois—while maintaining strong safety records and implementing estimating and process automation. Comfortable navigating political, stakeholder, and third-party complexity, he pairs field-born construction judgment with strategic planning and KPI-driven management. A Cal Poly Construction Management graduate, Drew also brings an unusual technical bent for the industry, having contributed code-level fixes to open-source scientific tooling earlier in his career.
16 years of coding experience
12 years of employment as a software developer
California Polytechnic State University, San Luis Obispo
Workflows and interfaces for neuroimaging packages
Role in this project:
Back-end Developer
Contributions:30 commits in 10 months
Contributions summary:Drew contributed to the AFNI (Analysis of Functional NeuroImages) software package by implementing and modifying classes within the `nipype/interfaces/afni.py` file. Their work involved adding new classes for AFNI commands like `Threedrefit`, `Threedresample`, and `ThreedTstat`. The user also focused on refactoring existing code, specifically modifying parsing functions to deal with argument order in the AFNI commands and fixing bugs in the handling of time-dependent parameters within the To3d interface. This included improving the handling of arguments within various AFNI commands.
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