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Databricks interview notes, page 3 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 SWE interview with a grid traversal problem that had a twist: you're not just finding a path, you're optimizing across two competing objectives at once. Felt like a pretty classic setup until the trade-off framing came in and I had to actually justify my algorithmic choice out loud.
  • Given a grid with a start and destination cell, find a path that optimizes a combined objective involving both the number of steps taken and a cost associated with cells or edges. You need to define the trade-off yourself (e.g. minimize cost within a step budget, or minimize a weighted combination), then pick and justify the right algorithm: BFS, 0-1 BFS, or Dijkstra.

“The part that tripped me up was that they wanted me to define the trade-off before coding anything.”

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Databricks·Software Engineer·Take-home Assignment

Apr 2026
Databricks SWE interview that threw a query optimizer problem at me. You get a tree of SQL plan nodes and have to implement real optimizations on it, not just describe them. Harder than I expected to get right under pressure.
  • Given a tree structure representing a SQL query plan, implement transformations to produce a more efficient equivalent plan. You must include predicate pushdown and at least one additional optimization such as projection pruning or constant folding. Your solution needs to run against a provided test harness.

“The predicate pushdown part felt manageable once I mapped out the cases where pushing a Filter below a Join is actually safe versus when it blows up semantically.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks SWE interview with a BFS grid problem. Pretty standard algorithmic round but there was a specific optimization they were pushing for.
  • Given a 2D grid, use BFS to find the shortest path (by time or cost) from a source to a destination. Follow-up: optimize the traversal so you only scan the grid once instead of making a separate pass for each direction.

“I got the basic BFS working fine but the optimization tripped me up a bit.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks systems design round, and they went straight for the jugular with a distributed fault-tolerance problem. The layered circuit breaker question was more involved than I expected and I kept second-guessing myself on the thread safety parts.
  • Design a two-layer circuit breaker (per-instance and global) that protects a downstream service, supporting CLOSED, OPEN, and HALF_OPEN states with configurable failure thresholds, a rolling window, and a cool-down period. The global breaker should influence local breaker behavior, and both must be passed for a request to go through. Walk through allow(), recordSuccess(), recordFailure(), state transitions, thread safety, and how you'd implement the metrics window.

“This one ate up the whole session.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks software engineering interview that went deep into networking fundamentals. The main problem was a firewall-style IP filtering question that touched parsing, bit manipulation, and data structures all at once. More involved than I expected for a single coding round.
  • Given a list of CIDR blocks each labeled ALLOW or DENY, and an IP address, determine whether that IP is allowed or denied. The rules include precedence logic like most-specific match wins or DENY overrides ALLOW. You need to implement CIDR parsing, convert IPs to integers, and find matches efficiently using something like a bit-level trie. Also be ready to discuss how your approach changes for IPv6.

“This one took me a minute to even get oriented.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks system design round, one big question about building a job scheduler with DAG-based task dependencies. The scope was broader than I expected and we spent the whole session on it.
  • Design a job scheduler where each submitted job is a DAG of tasks with dependency relationships. Tasks with satisfied dependencies should run in parallel up to a configurable worker pool size. The system should detect cyclic graphs, surface per-task and per-job status, handle failures and retries, and support cancel and status query APIs.

“This one ran the full session.”

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Databricks·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Databricks system design round, two meaty problems back to back. The focus was entirely on distributed systems thinking: fan-out patterns, tenant isolation, and back-pressure. Left feeling like I handled the first part okay but got a bit shaky when they pushed on tail-latency specifics in the second half.
  • Design a batch quote endpoint for a bookseller marketplace where a buyer submits a list of book IDs and quantities, and the system aggregates live pricing and availability from multiple third-party seller services in real time. How do you handle fan-out to those sellers, timeouts, partial responses, and caching?
  • A single large buyer is flooding the order-creation service and degrading performance for everyone else. How do you design isolation so one tenant can't impact others, covering per-tenant rate limiting, queue or thread pool separation, and tail-latency protection?

“This is where I spent most of my energy.”

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Databricks·Software Engineer·Hiring Manager Screen

Apr 2026
Behavioral hiring manager round for a Solution Architect role at Databricks. Basically a resume walkthrough with a heavy focus on pre-sales experience, so if your background is more internal-facing you might feel a bit exposed here.
  • Walk me through your resume, specifically the parts where you worked directly with customers in a technical capacity.
  • Tell me about a specific deal or account where you ran a proof of concept or technical demo. How did you approach scoping it?
  • How do you tailor a demo for different types of stakeholders or customer environments?
  • Describe a time you ran into a technical objection during a customer conversation. How did you handle it?
  • Give me an example of a project where you had to collaborate closely with internal teams to deliver something for a customer.
  • Tell me about a situation where you took ownership of a problem that wasn't strictly your responsibility.

“This is the core of the whole conversation so you can't treat it as a warmup.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks data engineering interview, one round focused entirely on a revenue aggregation problem with two fairly involved follow-ups. The core question was approachable but the extensions pushed into system design territory pretty fast.
  • Given a list of customer-revenue records, each with a customer ID and an amount, aggregate total revenue per customer and return the bottom-K customers by revenue. How do you handle ties?
  • How would you redesign the aggregation if the revenue field were nested, say broken down by product category or sub-account hierarchy? Discuss the design and complexity tradeoffs, no need to implement.
  • How would you optimize this system differently for a read-heavy workload versus a write-heavy one? And how does the design change if records are arriving as a continuous stream?

“Pretty standard aggregation problem.”

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Databricks·Software Engineer·Technical Phone Screen

Apr 2026
Databricks coding interview with a networking/IP filtering problem that felt deceptively algorithmic once the follow-ups started piling on. The core question was clear enough but the edge cases around overlapping rules kept me second-guessing myself the whole way through.
  • Given a list of CIDR rules each labeled as approve or reject, and a single query IP address, determine whether that IP is approved or rejected. You need to handle the matching policy (longest-prefix-match vs first-match-wins), parse CIDR notation into network ranges, check IP membership, and build an efficient lookup structure for many rules.

“I spent probably two minutes just clarifying the matching policy before writing a single line, which in hindsight was the right call because the interviewer seemed to appreciate it.”

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