I applied online. I interviewed at Snowflake (Menlo Park, CA) in Apr 2026
Interview
I was given a Take Home assessment on Snowconvert AI tool and told to migrate couple of Stored Procedures to Snowflake. Then I was selected to a Behavioural round which was easy, but I was not completely prepared
Interview questions [1]
Question 1
How did you use snowconvert AI?
What are the things you want to improve in snowconvert AI
"Overall, it took about three weeks to hear back after my initial phone screen, which felt longer than expected for a role like this. The technical round involved a DSA question on grouping anagrams, where I discussed my hashmap approach and its time complexity. a few days earlier. While I felt prepared, I stumbled on a follow-up question about Unicode handling, which impacted my confidence. Eventually, I got the no-offer news, but it was a good learning experience."
Looking back, I’m relieved it’s over — the interviews were tougher than I expected. The process kicked off with a technical screen that was brutal; they asked me to design a metadata service for columnar storage and discuss sharding strategies. I had practiced a similar scenario on prachub.com just a week before, and it helped a lot. The final round was filled with complex system design questions that left me drained. Ultimately, I received an offer, but the overall experience didn’t align with what I was looking for, so I declined.
Interview questions [1]
Question 1
Design a metadata service that tracks micro-partitions for a columnar storage engine handling petabyte-scale tables. Walk through how you'd shard the metadata across nodes, handle concurrent table updates without locking entire partitions, and prune partitions during query planning using min/max statistics. Then implement the in-memory pruning index in code, including how to merge per-column statistics after a background compaction job rewrites partitions.