Just finished my Databricks senior SWE loop (they call it L5 internally, roughly equivalent to L5 at Google or E5 at Meta) and want to write up the system design portion while it's still fresh.
The system design round was 60 minutes. Single interviewer who was pretty senior. The problem they gave me was essentially: design a distributed data processing pipeline that handles streaming ingestion at high volume with exactly-once semantics. Very on-brand for Databricks given their whole Spark / Delta Lake heritage.
A few things that stood out:
They care about trade-offs more than textbook answers. I started rattling off components and the interviewer interrupted me pretty early to ask "what's the cost of that choice?" I shifted into trade-off mode and the energy in the room changed noticeably.
Fault tolerance and idempotency came up hard. Not just "add a retry" but specifically: what happens when a consumer dies mid-batch? What's your checkpointing strategy? This maps directly to how Delta Lake actually works, so if you prep, read up on Delta's ACID transaction model and WAL.
Data locality matters. They asked me how I'd handle data skew in a distributed aggregation. If you've never dealt with skew in Spark or similar, get your story straight. A solid answer is: detect via percentile stats on partition sizes, and repartition by a salted key.
Scalability numbers were probed. "Assume 10TB/day of incoming events. Walk me through your capacity estimate." Standard stuff but they want you to actually do the math out loud.
The design itself wasn't exotic. The differentiation was in how confidently I could talk about failure modes. L5 at Databricks is expected to design things that survive the real world, not just a whiteboard.
Prep I'd recommend: read through Delta Lake's technical blog posts (they're actually well-written), brush up on Kafka consumer group semantics, and practice Dynamo-style availability vs. consistency trade-offs. The problems aren't unique to Databricks but the framing is very data-infra flavored.
Ask me anything specific.