Anduril Industries Data Engineer Interview Guide

Anduril's loop is filtering for engineers who can own infrastructure decisions with incomplete specs and real operational stakes. The company builds hardware that operates in contested environments, and the pipelines feeding those systems can't be debugged in production the way a typical SaaS backlog can. What the interviewers are listening for isn't just correctness: it's whether you reason about failure modes before someone asks, whether you push back on ambiguous requirements with a specific question rather than an assumption, and whether you think about the consumer of your pipeline, often an autonomous system, as a first-class constraint. Engineers who describe their past work in terms of what the data enabled, not just what they built, read better here than those who lead with tooling choices.

Start with pipeline architecture fundamentals: batch and streaming trade-offs, partitioning and backfill strategies, and how to handle schema evolution in a system where the consumer can't tolerate silent data quality regressions. That's where the loop concentrates and where a weak answer ends the conversation early. Next, sharpen your SQL, Python and PySpark fluency enough that the screen doesn't become a distraction from the design conversation. Leave trivia prep and generic SQL drills for last; 24 in the salary pool skew toward L3 and L5, so the bar at L5 reflects 9 years of median experience and interviewer expectations calibrate accordingly. If you're coming in at the senior level, the question isn't whether you can build the pipeline; it's whether you can defend why you built it that way when requirements shift mid-program.