10 Databricks Software Engineer (New Grad) Interview Questions (2026)
Databricks' new-grad SWE loop in 2026 is a recruiter screen, one OA, one technical phone screen, and a four to five round virtual onsite. Coding rounds skew medium-hard with a focus on data-structures depth and distributed-systems intuition. Open-source contributions and exposure to Spark, Delta Lake, or MLflow stand out in resume screens.
By Alex Chen, Founder, InterviewChamp.AI · Last verified
Loop overview
New-grad timeline reports show 5-7 weeks from recruiter outreach to offer in 2026. The flow is OA (HackerRank, 60-90 min) → 45-min phone screen → four to five virtual onsite rounds. Onsite is typically two coding rounds, one design-leaning round (lightweight for new-grad), one behavioral, and sometimes a domain round for ML or distributed-systems candidates.
Behavioral (3)
Tell me about a time you had to learn a new technology fast. How did you approach it?
Frequently askedOutline
STAR. Pick a concrete instance with a clear timeline (had two weeks, had one hackathon, etc.). Show the learning sequence: docs → tutorials → toy build → real use. End with the outcome and what stuck. Databricks engineers ramp on new tools constantly; they want to see structured learners.
Why Databricks? What do you find interesting about data and AI infrastructure?
Frequently askedOutline
Show real interest in the open-source ecosystem (Spark, Delta Lake, MLflow) or the lakehouse architecture concept. Reference a course project where you used Spark or pandas-at-scale, or an open-source contribution you read about. Generic 'big data is exciting' answers underperform.
Tell me about a time you received critical feedback. How did you respond?
Frequently askedOutline
STAR. Pick a real instance where the feedback was uncomfortable but correct. Show you did not get defensive, you asked clarifying questions, you took specific action. End with a measurable change in your behavior or output. Databricks screens for growth mindset.
Coding (LeetCode patterns) (4)
Given a list of integers, return the contiguous subarray with the largest product.
Frequently askedOutline
Track both max and min ending at current index (because a negative can flip a min into a max). On each step: new_max = max(current, current * prev_max, current * prev_min), new_min = min(...). Update global max. O(n) time, O(1) space. Edge case: zero in the array resets both.
Given two sorted arrays, merge them in-place into the first (which has enough space at the end).
Frequently askedOutline
Three pointers, all moving backward: write at end of nums1, read from end of nums1's filled part, read from end of nums2. Compare and write the larger. Avoids O(n) shifts. O(m+n) time, O(1) space. Walk through carefully — pointer arithmetic trips people up.
Implement a function that returns the kth largest element in an unsorted array.
Frequently askedOutline
Three approaches to discuss: (1) sort, O(n log n), simple. (2) min-heap of size k, O(n log k), good for streaming. (3) quickselect, O(n) average, O(n^2) worst, often the expected answer at Databricks for the perf discussion. Walk through quickselect's partition logic.
Given a directed graph, determine if it contains a cycle.
Occasionally askedOutline
DFS with three node states: unvisited, in-progress, visited. If during DFS you hit an in-progress node, there is a cycle. O(V+E) time, O(V) recursion space. Alternative: topological sort via Kahn's algorithm — if you cannot consume all nodes, there is a cycle.
Technical (3)
Design a key-value store interface (get, put, delete) backed by an LSM tree at a conceptual level.
Occasionally askedOutline
Writes go to an in-memory memtable, flushed to immutable SSTable files on disk. Reads check memtable, then SSTables newest-to-oldest. Background compaction merges SSTables. Discuss read amplification, write amplification, and bloom filters for read-skip optimization. Databricks values candidates who can explain storage engines at this level.
Explain how distributed query execution works at a high level. What problems does it solve?
Occasionally askedOutline
Query is parsed centrally, then a logical plan is split into stages. Each stage runs as parallel tasks on data partitions. Shuffles redistribute data between stages (e.g. for a join). Discuss bottlenecks: data skew, network bandwidth, straggler tasks. Databricks values understanding of why partitioning + shuffles dominate query cost.
Design a rate limiter that allows N requests per minute per user.
Occasionally askedOutline
Token bucket or fixed/sliding window. Token bucket: each user has a bucket of N tokens that refills at N per minute; each request consumes one. Sliding window log: store request timestamps per user, expire old ones, count remaining. Discuss in-memory vs distributed (Redis) implementation, and the tradeoff between accuracy and storage.
Databricks interview tips
- Open-source contributions to Spark, Delta Lake, MLflow, or any data-infrastructure project earn real attention from Databricks recruiters. Even one merged PR on a popular OSS project helps the resume bypass.
- Distributed-systems intuition matters even at the new-grad level. Be able to talk about partitioning, shuffles, fault tolerance, and consistency models at a conceptual level.
- Coding rounds at Databricks favor candidates who write production-quality code, not just correct code. Use meaningful variable names, handle edge cases, and write small helper functions when they clarify intent.
- Behavioral rounds probe technical curiosity. Have a project or technology you can speak about for ten minutes with genuine enthusiasm.
- Databricks moves fast on offers for strong candidates. If you have a competing offer with a deadline, tell your recruiter early — they can compress the timeline.
Frequently asked questions
How long is Databricks' SWE new-grad interview process in 2026?
Most reports show 5-7 weeks from OA to offer. Hiring committee review adds 1-2 weeks. Referrals can compress the timeline to 3-4 weeks.
Does Databricks ask system design for new-grad SWE?
A lightweight design-leaning round is common. New-grads are not expected to design full distributed systems — show structured thinking, mention tradeoffs, and ask clarifying questions. Senior new-grads with relevant projects may get a deeper design round.
How heavy is the open-source bar for Databricks SWE?
Not required, but helpful. Open-source contributions to Spark, Delta Lake, MLflow, or similar projects strongly accelerate the resume screen and give interviewers concrete material to discuss.
What programming languages does Databricks use most?
Scala and Java dominate the core engine. Python is used heavily for SDKs and ML tooling. The interview itself accepts any language; production stack knowledge is not tested at the new-grad level.
Does Databricks hire remote SWE new-grads?
Some teams hire remote, but most new-grad roles are in-office or hybrid in Bay Area, Seattle, or Mountain View. Confirm with your recruiter — role-specific.
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