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Databricks interview notes, page 5 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

Apr 2026
Databricks coding screen, one question the whole time. It's a classic sliding window problem dressed up as a system design-ish thing, and I spent way too long overthinking the data structure before just going with a queue.
  • Design a hit counter that tracks the number of hits in the past 300 seconds. Implement a hit(timestamp) method to record a hit and a getHits(timestamp) method to return the count of hits within the last 5 minutes.

“I went straight to a hashmap and immediately started second-guessing myself when they asked about memory.”

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Databricks·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral portion of a Databricks software engineering onsite. Two questions, both pretty standard but they clearly care a lot about how you frame ownership and what you actually learned from hard moments.
  • Tell me about a project you're proud of.
  • Describe a time you received critical feedback. What was the feedback, how did you react, and what changed afterward?

“I went with a project that had a clean before-and-after metric, which helped a lot.”

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Databricks·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Databricks ML Engineer interview with a pretty gnarly custom data structure problem. The core challenge was designing operations on an interval-based ciphertext abstraction, which sounds academic until you're staring at it live.
  • You have a string called cipherbook and a cover, which is a list of (start, end) index intervals that select ranges from cipherbook to form a ciphertext. Implement a delete operation: given an index into the resulting ciphertext, remove that character and return the updated cover. The deletion might shrink an interval or split one interval into two.
  • Implement a simplify operation on the cover: merge any consecutive intervals whose cipherbook ranges are contiguous or overlapping, so the cover has no redundant splits.
  • What data structures would you use to efficiently find which interval a given ciphertext output index falls into, and what is the amortized complexity when you do many deletions?

“The split case is what gets you.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks system design round that went deep into networking fundamentals pretty fast. The question started as a CIDR explainer and turned into a full firewall rule engine with some nasty follow-ups about range queries and IPv6. Felt like two interviews in one.
  • Explain CIDR notation with concrete examples. For a prefix like 192.168.0.0/16, show how to derive the inclusive 32-bit integer range and the corresponding subnet mask.
  • Design a firewall rule system where each rule has a CIDR prefix, an allow/deny action, and a priority. Implement addRule, removeRule, and a query that takes a single IP address and returns the matching action. Clarify your matching semantics for overlapping prefixes, supporting both first-match-by-priority and longest-prefix-match modes.
  • How would you scale this firewall rule system to handle up to one million rules and one hundred thousand queries per second? What are your target time and space complexities?
  • Now the query input is a CIDR block rather than a single IP. Add operations: overlap(queryPrefix) returning a boolean, coveredByAllow(queryPrefix) and coveredByDeny(queryPrefix) each returning a boolean, and listConflictingRules(queryPrefix) returning a list of rule IDs. What data structures would you use for efficient range and overlap queries?
  • How would you extend this entire design to support IPv6 addresses?

“Felt like a warmup but it wasn't.”

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Databricks·Data Scientist·Technical Phone Screen

Apr 2026
Databricks data scientist interview that leaned heavily into regression fundamentals, specifically around multicollinearity. The questions felt more academic than I expected from a company like Databricks, but they went deep fast.
  • What is multicollinearity, and why might it show up among predictors like age, education, and years since degree in a gender-income regression?
  • How would you actually check for multicollinearity in a dataset? What specific diagnostics or metrics would you use?
  • If multicollinearity is present, what are your options for dealing with it, and how do you weigh interpretability against predictive performance?

“I explained the definition fine but fumbled a bit connecting it to the specific predictors.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks system design round, one meaty question about building a concurrent log writer. The kind of problem that sounds manageable until you're 20 minutes in and realizing you've been hand-waving the hard parts.
  • Design a thread-safe log writer that can handle many concurrent producers. It needs a log(level, message) API that doesn't block producers for long, batches and flushes messages in submission order to a file or stdout, survives high contention without losing messages on shutdown, and exposes flush() and close() methods. Walk through your synchronization approach, how you'd handle back-pressure if the queue fills up, and how you'd test for race conditions.

“I started with the obvious answer: a lock-protected queue and a single background writer thread draining it.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks data engineer interview that zeroed in on pipeline architecture decisions. Just one meaty design question but it required knowing the DLT product well enough to argue for or against it.
  • When would you choose Delta Live Tables over building a pipeline directly on Spark and Delta Lake, and what are the trade-offs of each approach?

“This one exposed a gap in my thinking.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks data engineer interview with a scenario-based question about fault tolerance in scheduled pipelines. Pretty focused on cost-aware reliability, which felt more practical than the usual theoretical stuff.
  • A task in a nightly pipeline fails unexpectedly about 10% of the time. How do you make sure the job completes successfully every night without running up compute costs?

“The cost constraint is what makes this interesting.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
One round at Databricks for a software engineering role, and the interviewer threw something I'd genuinely never seen before. Not your typical graph problem.
  • Given a list of node groups (e.g. [[1],[2,3],[4,5,6]]), connect all groups into a single connected graph using the minimum number of edges (k groups need k-1 edges). Each edge must be formed by picking one node from each of two different groups. The output must be uniformly random across all valid spanning structures.

“The minimum edges part clicked for me pretty fast, basically just a spanning tree over groups.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks coding round, got a Snake game design problem which I was not expecting at all. Thought it'd be a straightforward grid traversal thing but the details add up fast.
  • Design and implement a Snake game on a grid of given dimensions. The move(direction) function should advance the snake one step in the given direction and return the current score, or -1 if the snake hits a wall or itself. Food appears at predetermined positions and eating it grows the snake and increments the score.

“My first instinct was to just track the head and recompute everything, which would have been a mess.”

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