Databricks

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·Backend Engineer·Technical Phone Screen

Jul 2026

Databricks backend round, pretty algorithmic in nature. The main problem was a sliding window thing and then they pivoted to a system design follow-up that I was not expecting at all.

Given two strings, find the starting index of the first substring in the first string that is an anagram of the second string. Return -1 if none exists.
How would your approach change if the input text was extremely large, streamed in chunks, or spread across multiple machines?

“Knew immediately it was a sliding window with frequency counts.”

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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·Data Scientist·Technical Phone Screen

Jul 2026

Databricks data scientist interview with a meaty technical question on evaluation metrics. Just one question but it had six sub-parts and they clearly wanted you to go deep, not just recite definitions.

You've trained a binary classifier that outputs predicted probabilities. Walk through ROC-AUC vs PR-AUC: what each curve actually plots, how class imbalance affects them, when they disagree, which you'd prefer for model selection, how you'd think about it if the business goal is catching as many positives as possible within a fixed review budget, and what practical pitfalls exist with either metric.

“This is the kind of question where you think you know it and then halfway through you realize you're just reciting things you memorized.”

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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 interview FAQ

What rounds are in the Databricks interview?

Candidates most often report a technical phone screen (136 write-ups), a system design round (57), a behavioral / leadership round (9) and an onsite coding round (6).

What does the Databricks technical phone screen cover?

Across 136 technical phone screen write-ups, the most tagged question topics are Algorithms & Data Structures (157 questions), Technical Trade-offs (150) and System Design (99).

What is asked in the Databricks system design round?

Across 57 system design write-ups, the most tagged question topics are System Design (143 questions), Technical Trade-offs (119) and Algorithms & Data Structures (36).