Databricks Software Engineer Interview Questions

Databricks·Software Engineer·Technical Phone Screen

Jul 2026

Databricks SWE interview threw a full codec design problem at me, streaming compression with RLE and bit-packing. More involved than I expected for a single session, felt like a take-home crammed into live coding.

Design and implement a streaming integer codec that supports two encoding strategies: run-length encoding for repeated values and bit-packed encoding for variable values. The encoder receives values one at a time and must decide per block which strategy to use.
Define the exact in-memory structure for both RLE and bit-packed blocks, including how the bit-packed payload is stored and how bit-width is determined per block.
Implement the Decoder as an iterator that reconstructs the original integer sequence from a list of encoded blocks, handling both RLE and bit-packed block types correctly.
Write unit tests covering: sequences with no repeats, long single-value runs, alternating RLE and bit-packed patterns, edge values like Integer.MAX_VALUE and Integer.MIN_VALUE, and the empty input case.

“This one took me a minute to even parse.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jul 2026

Databricks system design round focused entirely on building a versioned, snapshot-capable set data structure from scratch. The question had a lot of moving parts and the design discussion went deep fast.

Design a SnapshotSet data structure supporting add, remove, contains, snapshot (returning a snapshot ID), and iterate over a past snapshot. It needs to handle many concurrent snapshots while the live set keeps changing, with O(log n) updates and O(1) amortized iterator next.
Walk through the memory usage and snapshot reclamation strategy for your design. How do you free memory from old snapshots that are no longer referenced?
What are the trade-offs between copy-on-write, a persistent balanced tree, and versioned hash buckets for this use case?
Describe the unit tests you would write for this data structure, specifically covering an empty set, deletions before and after a snapshot, and concurrent mutation during iteration.

“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 round focused on concurrency in a key-value store and hit counter. The depth they expected was pretty serious, way beyond just naming mutex vs rwlock.

Given a key-value store and a hit counter running under concurrent access, identify the race conditions that could arise, propose thread-safety mechanisms ranging from coarse-grained locks to lock-free designs, discuss the correctness and performance trade-offs of each approach, and explain how you would test and validate the concurrency behavior.

“I started with the obvious stuff, coarse lock over the whole map, then moved to read/write locks since reads dominate in a hit counter scenario.”

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Databricks·Software Engineer·Recruiter / HR Screen

Jul 2026

Behavioral screen for a software engineering role at Databricks, basically just a walk me through your background question. Pretty standard stuff but I fumbled a bit trying to tie everything together into a coherent story.

Walk me through your background, covering your most relevant roles, the technologies you worked with, the scale of systems you built, and the impact you had. What drove each career move, and why does this role fit where you want to go?

“This is the kind of question that feels easy until you're actually in it.”

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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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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·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 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·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

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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