Databricks interview notes, page 15 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·Onsite - System Design / Architecture

Jun 2026

Databricks system design round focused entirely on building a single-machine cache from scratch. The depth they expected was pretty serious, from pseudocode to concurrency reasoning to eviction policy internals.

Design a low-level single-machine cache for a web service that handles incoming requests. Write pseudocode and walk through your design decisions.
How would you reduce the size of your critical sections in the cache implementation, and what concurrency risks does that introduce?
Walk through how you would reason about deadlocks in your sharded cache design.
How would you extend this single-machine cache design to a multi-machine setup?

“The 'low-level' part is what trips people up.”

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Databricks·Software Engineer·Technical Phone Screen

Jun 2026

Phone screen at Databricks, one question, system design flavor but kept pretty contained. Not a brutal round but the QPS tracking piece has some gotchas if you haven't thought about sliding windows before.

Design a key-value store that supports put, delete, and a method to query the average queries per second over the last five minutes.

“The put and delete parts are easy enough that you might spend too long on them and then scramble when they ask about the QPS tracking.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Databricks system design round, one big question about building a durable multi-producer log writer. Dense topic and they clearly wanted you to go deep on every sub-problem, not just sketch an architecture and move on.

Design a log writer that supports concurrent producer threads and guarantees each accepted log entry is durably written to disk (fsynced) before returning success. Cover your locking strategy, how you'd batch writes to amortize fsync cost, ordering guarantees across threads, back-pressure when the disk can't keep up, and crash safety.

“This question is basically five questions jammed into one.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Databricks system design round where they handed me a bare interface skeleton and told me to build a key-value store with a QPS tracking API. The requirements clarification part was basically the whole interview, which I wasn't expecting.

Design and implement a key-value store with put(key, value), get(key), and a QPS method that returns queries-per-second over a rolling time window. You're given only an interface skeleton to start.

“The interface skeleton thing threw me.”

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Databricks·Software Engineer·Technical Phone Screen

Jun 2026

Databricks data engineering interview with a coding problem focused on revenue aggregation and heap-based selection. Pretty standard algorithmic stuff but the edge case discussion was more involved than I expected.

Given a list of (customer_id, revenue) records, aggregate total revenue per customer and return the bottom K customers by total revenue, breaking ties by customer_id ascending.

“I went with heapq.nsmallest after building the aggregation dict in one pass, which felt right.”

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Databricks·Software Engineer·Technical Phone Screen

Jun 2026

Databricks software engineer interview with a project deep-dive format. You pick one project you owned fully and walk through everything: context, design decisions, trade-offs, implementation, outcome. The follow-ups were the real interview.

Walk me through a project you owned end-to-end: the context, your role, the technical design, key decisions and trade-offs, implementation, outcome, and what you took away from it.

“This sounds straightforward but the follow-ups are where it gets real.”

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Databricks·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026

Databricks ML Engineer interview that went deep into content moderation system design. The scope was broad enough that I kept second-guessing whether I was going too deep on any one piece or skimming things I should've owned.

Design an end-to-end ML system for detecting harmful content (hate speech, violence, sexual content, self-harm, harassment) on a user-generated content platform.
How would you frame this as a modeling problem across different content types like text, images, video, and audio?
Walk me through how you'd build the data labeling pipeline, and how you'd handle class imbalance and adversarial content drift.
How would you approach real-time inference at upload versus async batch rescanning, and what latency constraints would you design around?
How would you design the human review and escalation workflow, including how you'd calibrate confidence thresholds against reviewer cost?
What metrics would you use to evaluate this system, and how would you balance precision and recall across different policy categories?
How would you ensure the system is robust against adversarial manipulation, fair across demographic groups, and effective across multiple languages?

“This is a monster of a question and I did not pace myself well.”

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Databricks·Data Scientist·Technical Phone Screen

Jun 2026

Interviewed for a Data Scientist role at Databricks and got hit with a pretty theoretical linear regression question that had more depth than I expected. The whole thing felt like a math exam disguised as a conversation.

You have two regressors x1 and x2. Model A is linear in x1 and x2 directly. Model B is linear in the transformed features (x1 + x2) and (x1 - x2). Are these two models equivalent? Walk through the math.
If you have more than 1,000 predictors and want to fit a linear model, what issues could come up and how would you handle them?

“I knew instinctively they were equivalent but fumbled the explanation for a moment.”

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Databricks·Software Engineer·Technical Phone Screen

Jun 2026

Databricks technical phone screen, probably for a backend or infrastructure-adjacent role. The whole thing was one meaty networking/algorithms problem that took most of the session to get through properly.

Given a list of CIDR blocks, merge any overlapping or adjacent ranges and return the minimal set of CIDR blocks that covers exactly the same IP address space.

“This one has layers and I did not appreciate that until I was already in trouble.”

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Databricks·Technical Program Manager·Technical Phone Screen

Jun 2026

TPM interview at Databricks, just the one question from what I can tell. Short and technical, focused on observability for microservices.

How would you collect health and observability data across deployed microservices?

“I went straight to metrics and logging pipelines, talked about aggregating service health signals and setting up alerting thresholds.”

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