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Databricks interview notes, page 7 of 22

Large Enterprises

Most of these Databricks write-ups come from Software Engineers (192), then Machine Learning Engineers (9) and Data Scientists (8). The most reported rounds are the technical phone screen (136), the system design round (57), the behavioral / leadership round (9) and the onsite coding round (6).

216 interview notes · updated Jul 2026

Databricks·Software Engineer·Technical Phone Screen

May 2026
Screened for a software engineer role at Databricks and got a design question that felt more product/UX than engineering. Short round, one question, left me second-guessing what they were actually testing for.
  • Design a bookstore.

“Wasn't sure if they wanted a system design answer or a product walkthrough.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks software engineer round focused on building a key-value cache from scratch, with follow-ups on testing and persistence. Pretty implementation-heavy, less hand-wavy than I expected for a system design adjacent question.
  • Implement a key-value cache supporting put, get (with a hit counter), and hits operations. Be ready to discuss thread-safety and eviction.
  • Write unit tests covering the cache's put, get, miss, and hits behavior, including overwrites, repeated gets, and absent key lookups.
  • How would the cache design change if it needed to be persistent or disk-backed, for example using an LSM-tree style write path?

“I started with a plain dict and got the basic operations working pretty fast.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Databricks for a software engineer role, centered entirely on designing an online bookstore from scratch. Pretty broad scope and they wanted to go deep on several areas at once, which made it hard to pace well.
  • Design an online bookstore service covering catalog browsing, search, shopping cart, checkout, payments, order management, user accounts, reviews, inventory, and recommendations.
  • How would you design the API for the bookstore, and what does your data model look like for entities like users, books, inventory, carts, orders, and payments?
  • How would you handle search across a large book catalog with low-latency requirements?
  • How would you ensure inventory consistency and make payment processing idempotent, especially during peak traffic?
  • Walk through the full order and payment flow end to end, including how you'd handle failures and retries.
  • Where would you apply caching in this system and what are the tradeoffs?

“The scope was massive and I tried to cover everything which was a mistake.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks software engineering interview with a pretty gnarly networking/CIDR problem. The question had enough moving parts that I spent a good chunk of time just making sure I understood the semantics before writing a single line.
  • Given a target IPv4 CIDR block and an ordered list of allow/deny rules (each with their own CIDR), implement a function that returns true if the target block is fully covered by allow rules without any deny rule overlapping the remaining uncovered region. Rules must be processed sequentially, and allow rules subtract their overlap from what remains; a single deny overlap with remaining IPs returns false immediately.

“This one took me a while to even parse correctly.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks SWE interview that was basically one long design question about a hit counter, but with enough layers that it kept unfolding for the whole session. Thought I had it figured out after the basic implementation, then they kept pulling on threads.
  • Design a hit counter that supports recording a hit at a given timestamp and querying how many hits occurred in the past N seconds.
  • How do you handle multiple hits arriving at the same timestamp within the same bucket?
  • How would you generalize this implementation to support an arbitrary window size instead of a hardcoded 300 seconds?
  • Compare the fixed-size array approach to using a queue, deque, or hashmap with buckets. What are the trade-offs?

“Started with a queue, which felt natural, but they pushed back pretty fast on memory.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks SWE interview with a networking-flavored coding problem. Not the typical graph or DP question I was expecting, which threw me off a bit at first.
  • Build a function that takes a list of firewall rules (each mapping a CIDR block or exact IP to ALLOW or DENY) and an IP address, then returns the action from the first matching rule. A rule matches if the leading bits of the IP match the network portion defined by the prefix length.

“I hadn't touched CIDR notation since a networking class years ago so there was a solid 30 seconds of internal panic.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks SWE interview with a design question that looked straightforward on the surface but had a few gotchas worth thinking through carefully. The snapshot iteration part is where most of the interesting tradeoffs live.
  • Design and implement a mutable collection that supports snapshot-based iteration. The collection stores unique values and must support add, remove, contains, and an iterator() method that returns a snapshot of the collection at the time it was called. Mutations after calling iterator() should not affect what the iterator returns. Discuss time and space complexity for each operation.

“My first instinct was a HashSet with a synchronized copy on iterator() call, which works fine but I fumbled explaining why the lack of ordering requirements actually matters here.”

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Databricks·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Straight behavioral round for a software engineer role at Databricks. Nothing technically tricky, just the usual life-story and situational questions, but a few of them pushed harder than I expected.
  • Tell me about yourself.
  • Why do you want to work at this company and in this role?
  • Walk me through your internships and explain why you chose each one.
  • Tell me about a time you disagreed with a teammate or manager. How did you handle it?
  • What's the most challenging project you've worked on, and how did you approach it?
  • How do you handle situations where requirements are unclear or keep changing?
  • What's the most valuable piece of feedback you've received, and what did you do with it?

“Ran through my background and it felt fine, but I rambled a bit at the end trying to connect everything to Databricks specifically.”

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Databricks·Software Engineer·Technical Phone Screen

May 2026
Databricks coding screen, one question the whole time. Pretty focused session, they wanted to see if you could get past the naive O(n) check and actually think about the math behind it.
  • Design a Tic-Tac-Toe class for an n x n board. Implement a move method that places a mark for a given player and returns the winning player's number if that move wins, or 0 otherwise. The solution should run in O(1) per move.

“My first instinct was to scan the whole row and column after every move, which is obviously too slow.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

May 2026
Databricks system design round, one big question that took the full hour. The scope was genuinely wide and I kept second-guessing whether to go deep on one piece or keep moving. Not sure how I did.
  • Design a high-throughput client-side file cache that serves ranged reads from a remote storage service. The remote API lets you fetch by filename with byte offset and length, or download an entire file. Cover chunk sizing, request coalescing, prefetching, thread safety, deduplication of in-flight downloads, backpressure, eviction (LRU or similar), cache persistence, handling remote file changes, partial failures, retries, and timeouts. Define your read API and write policy, provide key data structures and pseudocode, and analyze correctness under concurrency plus time/space complexity.

“This one sprawled in every direction and I struggled to pick a thread.”

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