Thrilled to have accepted the offer from Snowflake after a challenging interview process. It all started with a technical phone screen where I tackled a DSA question on course dependencies and cycles. The onsite included multiple rounds, including a system design question and behavioral interviews. The wild thing is, I had literally seen this exact problem on prachub.com just a week prior, which really helped me stay calm and focused. Overall, the intense but rewarding experience left me excited to join their talented team.
Interview questions [1]
Question 1
There are a total of n courses you have to take, labeled from 0 to n-1. Some courses have prerequisites. Given the total number of courses and a list of prerequisite pairs, determine if you can finish all courses (detect a cycle in the dependency graph).
"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.
