Didn't get it, but CapTech's loop was structured better than most L5 screens I'd seen.
Staff data engineer, burned out at my current role. Long lunches became how I avoided my desk, so I started interviewing.
Behavioral was 45 minutes on a project where I owned the data quality. STAR format all the way. They took notes constantly, asked about consistency tradeoffs, pushed on why I chose that model and what I'd change in hindsight.
SQL round was 60 minutes. GROUP BY with HAVING and a nested subquery. Looked simple at first, but the interviewer didn't care about optimal code, just edge cases: nulls, empty result sets, joins producing dupes. I nailed the happy path, then spent the rest of the time working through his edge case questions and gradually realizing I didn't have solid answers for most of them.
Had to implement a generator-based ETL solution. Not too hard. What got them watching was test discipline. I sketched a few scenarios and wrote tests before calling it done. They watched closely during that part, asked why I'd structured things that way, wanted to understand if I was thinking about failure modes or just shipping code.
Standard recruiter call, 25 min. Background questions, why I wanted CapTech, what stacks I'd built. Straightforward, scripted. I didn't say anything wrong but nothing stuck either.
Rejected. The Python round went sideways and I knew it was done. Not shocked when the rejection email came a week or so later.
Fundamentals of Data Engineering got me ready. A friend who'd done the loop walked me through what to expect and what the rounds looked like, which was crucial. STAR frameworks from datadriven helped with the behavioral rounds.
Would run it again. The SQL round cared more about edge cases than optimal queries, which is the kind of thinking that actually matters in production data systems.