Bnsf Railway Data Engineer Interview Guide

I liked the process overall, even though it was a little more scattered than I expected. The first step was an online assessment on Codility that mixed Python and YAML, and I was given training examples plus about a week and a half to complete it. I had gone in expecting Python algorithms, but the actual test leaned more toward PySpark and infrastructure-style work. One question asked me to use PySpark to read a CSV, do a simple aggregation, and return the result as a dict. Another asked me to write a Kubernetes configuration in YAML based on provided specs. The assessment itself took about an hour, and the hardest part was really the mismatch between the job posting and what showed up in the test, not the difficulty of the coding.

After that, I moved into a technical round where they asked about my current project, my technical skills, and one system design question. That part felt more like a conversation about how I think through architecture and what I’ve actually built, rather than a pure coding screen. In the full-day loop, there were also behavioral rounds, including questions like a time I disagreed with a senior team member and explaining one of my technical strengths in a non-technical way. The recruiter was informative and responsive throughout, and the whole process stretched to around 60 days, which seemed pretty normal given the holiday timing and scheduling. I didn’t get an offer, but the process was clear enough once it got going. My main takeaway is to prep specifically for PySpark and YAML/Kubernetes-style tasks, not just generic Python interview problems, and be ready to talk through project architecture and system design at a high level.