Databricks Interview Questions — Page 21

Databricks·Software Engineer·Technical Phone Screen

Jun 2026

Databricks coding interview with a pretty gnarly tree problem that requires you to navigate a recursively defined structure without ever building it. The math is elegant once it clicks but getting there under pressure is a different story.

Given a k-order Fibonacci tree where nodes are labeled in preorder from 0 to size(k)-1, and the tree is defined recursively with a (k-2)-order left subtree and a (k-1)-order right subtree, find the path between two node ids a and b without constructing the tree in memory.

“This one took me a while to even understand what was being asked.”

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Databricks·Software Engineer·Technical Phone Screen

Jul 2026

Databricks SWE coding round, pretty much a sliding window problem the whole way through. They pushed hard for the linear solution so knowing your way around the naive approach isn't enough here.

Given two strings, find the starting indices of all substrings in the first string that are anagrams of the second string. What's the most efficient solution you can come up with?

“I started with the obvious brute force and they let me finish before asking if I could do better.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at Databricks for a software engineering role, focused entirely on building an online bookstore end to end. Pretty broad scope and they pushed hard on the tradeoffs between consistency models for different parts of the system.

Design an online bookstore system covering catalog browsing, search, cart and checkout, payments, order management, inventory, reviews, and recommendations.
How would you handle the consistency requirements differently for inventory and orders versus the reviews and recommendations parts of the system?
Walk through your data model for the catalog, inventory, and order domains.
How would you design the search index for the bookstore to achieve low latency at scale?
What caching strategy would you use to handle high read traffic, especially during peak times?

“The scope was massive and I think I spent too long on the catalog and search parts, then had to rush through payments and order management at the end.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Databricks system design round, focused entirely on a key-value store with a configurable sliding-window QPS tracker. The question kept unfolding into more trade-off territory than I expected and I felt like I was playing catch-up the whole time.

Design a key-value store that supports put, delete, and an average QPS query over a configurable sliding time window. Walk through how you'd handle different window sizes, memory costs, and the trade-offs between update and query performance.

“Started fine with the ring-buffer approach for a fixed 60-second window, per-second buckets, easy.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at Databricks for a software engineer role, focused entirely on building a durable key-value store from scratch. The depth they expected was pretty intense, covering everything from WAL design to concurrency models to back-of-envelope math. Left feeling like I'd been wrung out.

Design a durable key-value store with put, get, and delete operations. Walk through how you'd use a write-ahead log to guarantee durability, lay out your on-disk data structures, and explain how the system recovers after a crash.
How would you handle torn writes in your WAL, and what's your fsync and batching strategy?
Describe your compaction and garbage collection strategy for the on-disk data structures.
What concurrency model would you use: single-writer with multiple readers, or multiple writers? Would you use lock-based or optimistic concurrency, and how do you ensure correctness?
What consistency guarantees does your design provide, and how do read-after-write semantics work under concurrent access?
Do some back-of-the-envelope estimates for the resource requirements of this system.
What would a minimal testing plan look like for this system, specifically around failure injection and recovery?

“This was the core of the whole interview and it ate up most of the time.”

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Databricks·Software Engineer·Technical Phone Screen

Jul 2026

Interviewed for a solutions architect role at Databricks, and the main exercise was demoing LabelBox to an autonomous delivery client. Not a traditional interview format at all, more of a live product pitch scenario.

Walk us through a live demo of LabelBox tailored to the needs of an autonomous delivery client.

“This was basically the whole interview.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Databricks system design round focused entirely on concurrency primitives. One big meaty question about building a thread-safe queue from scratch, and they wanted you to go deep on basically every dimension of it.

Design and implement a thread-safe bounded queue that supports multiple producers and consumers, with blocking enqueue and dequeue operations that respect timeouts. How do you handle deadlocks, starvation, fairness, backpressure when the queue is full, and what's your locking strategy? Also discuss how you'd test it for both correctness and performance.

“This one sprawled in ways I didn't expect.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Databricks system design interview focused on building an in-memory key-value store for QPS tracking. The sliding window angle made it more interesting than a typical design question, and the follow-ups on space optimization pushed things further than I expected.

Design an in-memory key-value store that can calculate queries per second (QPS) over a rolling 5-minute window.
How would you reduce memory usage in your QPS tracking design?

“I went with a sliding window approach pretty quickly, which felt right.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Databricks system design round, one big question about distributed file storage that branched into like four different topics. Felt okay on the storage layout stuff but the delete pipeline section got messy toward the end.

Design a distributed file system that supports basic file operations (create, read, update, delete) with an emphasis on making deletion efficient at scale.
How do you handle CAP theorem tradeoffs in this system, specifically around metadata consistency versus data availability?
Walk through your delete pipeline. What happens from the moment a delete API call is made to when storage is actually reclaimed?
How does your delete and garbage collection approach interact with snapshots and ongoing replication?

“Started with chunked storage and replication, which felt solid.”

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Databricks·Software Engineer·Technical Phone Screen

Jul 2026

Databricks SWE interview that went deep on graph problems, specifically a commute optimization problem that kept getting harder as the conversation went on. The mode-switching extension was where things got interesting and also where I started to sweat a bit.

Given a graph where nodes are locations and edges are transit segments with associated costs or times, find the optimal route from a source to a destination. How would you approach this, and what algorithm would you use?
Now extend the problem: the traveler can switch between transit modes (walking, biking, car, public transit) along the route. Each mode has its own cost or time, and switching modes incurs a penalty. How do you model and solve this?
How does state space grow in this layered graph model, and how would you handle situations where not all transit modes are available at every node?
Would bidirectional search or reverse search help in this layered graph scenario? When and why?

“Pretty standard Dijkstra setup, I got through it fine.”

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