Databricks Interview Questions

Databricks Interview Questions

Practice 131 real Databricks interview questions for 2026. Covers all top categories — Coding & Algorithms, System Design, Behavioral & Leadership, Statistics & Math, Software Engineering Fundamentals — across Software Engineer, Data Scientist, Machine Learning Engineer, and Data Engineer roles. Real questions from actual interviews with detailed solutions; this collection explains what’s distinctive about Databricks interviews, what interviewers evaluate, what to expect in each round, and how to structure your interview preparation. Expect a heavy software-engineering tilt: Software Engineer rounds repeatedly probe storage and data-platform design (hierarchical file systems, cache designs, lakehouse tradeoffs), distributed job scheduling and dependency-aware pipelines, concurrency and multithreaded systems (synchronous log writers, thread-safe KV stores), and performance-focused algorithmic problems and query optimizations. Data Scientist questions emphasize regression assumptions and coefficient transformations, similarity search across datasets, hypothesis testing and metric tradeoffs (ROC-AUC vs PR-AUC), and product-analytics counting problems. Machine Learning Engineer prompts focus on implementing algorithms (gradient descent, lazy arrays) and safety/OOM detection for models. Data Engineer coverage targets data-quality diagnostics, Spark/partitioning and pipeline performance. To prepare, prioritize timed coding practice, system-design sketches that show tradeoffs for data systems, concise statistics explanations, and polished STAR stories for behavioral rounds.

131 Questions 1 Company07.30.2026
Showing 20 results
Role
Databricks logo
Databricks
Medium
Software Engineer

Implement CIDR firewall matcher

Implement CIDR firewall matcher Firewall Rule Matcher for IPv4 CIDR Rules Context and Assumptions You are to design and implement a firewall rule matc...

System Design
55
0
301 people solved
Aug 4, 2025
Databricks logo
Databricks
Medium
Software Engineer

Implement a snapshotable set with iterators

Implement a SnapshotSet data structure with the following API: add(x), remove(x), contains(x), snapshot() -> sid, and iterate(sid) -> iterator over th...

Coding & Algorithms
34
0
495 people solved
Aug 11, 2025
Databricks logo
Databricks
Hard
Software EngineerNew Grad

Implement streaming RLE and bit-packed codec

You are implementing a simple compression scheme for sequences of 32‑bit signed integers. The codec should support two encoding strategies: 1. Run‑Len...

Coding & Algorithms
50
0
677 people solved
Dec 10, 2025
Databricks logo
Databricks
Medium
Software Engineer Locked

Design RAG Retrieval for Data Assets

This question evaluates skills in designing hybrid retrieval pipelines over enterprise metadata, including competencies in information retrieval, exac...

ML System Design
37
0
378 people solved
May 2, 2026
Databricks logo
Databricks
Hard
Data Scientist

Explain ROC-AUC vs PR-AUC tradeoffs

Question You trained a binary classifier that outputs predicted probabilities. Compare ROC-AUC and PR-AUC (the latter usually reported as Average Prec...

Machine Learning
8
0
116 people solved
Dec 12, 2025
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Databricks
Medium
Software Engineer Locked

Design a Slack-like messaging system

This question evaluates system design and distributed-systems competencies, focusing on real-time messaging architecture, data modeling, scalability, ...

System Design
16
0
208 people solved
Dec 8, 2025
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Databricks
Medium
Machine Learning Engineer Locked

Implement Linear Regression Gradient Descent

This question evaluates understanding of simple linear regression, batch gradient descent, loss function formulation, gradient derivation for model pa...

Machine Learning
6
0
55 people solved
Jan 6, 2026
Databricks logo
Databricks
Medium
Software EngineerSenior+ Locked

Design bookstore and chat messaging systems

This question evaluates a candidate's competency in large-scale system design, including distributed systems architecture, data modeling, consistency ...

System Design
31
0
267 people solved
Oct 10, 2025
Databricks logo
Databricks
Medium
Software Engineer

Describe your background and impact

Walk Me Through Your Background (HR Screen — Software Engineer) Prompt Provide a concise walkthrough of your background focusing on: 1. Most relevant ...

Behavioral & Leadership
13
0
108 people solved
Sep 6, 2025
Databricks logo
Databricks
Medium
Software EngineerNew Grad

Design KV store with sliding-window average QPS

Problem Design an in-memory key–value store that supports mutation operations and can report the average QPS (queries per second) over a recent time w...

Coding & Algorithms
27
0
363 people solved
Dec 17, 2025
Databricks logo
Databricks
Hard
Software EngineerNew Grad

Find path in implicit Fibonacci tree

You are given a special family of binary trees called Fibonacci trees. The k‑th order Fibonacci tree T(k) is defined recursively: - T(1) is a single n...

Coding & Algorithms
136
0
1029 people solved
Dec 10, 2025
Databricks logo
Databricks
Medium
Software Engineer

Implement run-length encoding and decoding

You are given a string consisting of lowercase English letters. You need to implement run-length encoding (RLE) and its corresponding decoding. 1. Enc...

Coding & Algorithms
43
0
371 people solved
Dec 8, 2025
Databricks logo
Databricks
Medium
Software Engineer

Check if CIDR is fully canceled by rules

You are given: - A target CIDR block T as a string, e.g. "10.0.0.0/16". - A list of rule CIDR blocks. Each rule has: - A type: either "allow" or "de...

Coding & Algorithms
23
0
202 people solved
Dec 8, 2025
Databricks logo
Databricks
Hard
Software Engineer Locked

Implement an Alternating Tic-Tac-Toe Game

This question evaluates a candidate's ability to implement state management, alternating turn logic, input validation, board representation, game outc...

Coding & Algorithms
2
0
16 people solved
May 30, 2026
Databricks logo
Databricks
Medium
Software Engineer

Implement Snapshot Iterator Without Order Guarantees

Design and implement a mutable collection that supports snapshot iteration. The collection stores unique values and supports the following operations:...

Coding & Algorithms
1
0
13 people solved
Feb 16, 2026
Databricks logo
Databricks
Medium
Software Engineer

Answer behavioral screen questions

Answer behavioral screen questions HR Screen — Behavioral Questions (Software Engineer) Context: You are interviewing for a Software Engineer role in ...

Behavioral & Leadership
20
0
152 people solved
Jul 15, 2025
Databricks logo
Databricks
Hard
Software Engineer Locked

Determine allow/deny for an IP via CIDR rules

This question evaluates understanding of IPv4 addressing, CIDR range matching, rule ordering and precedence, and algorithmic correctness in determinin...

Coding & Algorithms
18
0
180 people solved
Feb 12, 2026
Databricks logo
Databricks
Medium
Software Engineer Locked

Design KV store with sliding-window QPS metrics

This question evaluates designing an in-memory key–value store with sliding-window throughput metrics, testing competencies in data structures, concur...

System Design
21
0
260 people solved
Nov 8, 2025
Databricks logo
Databricks
Medium
Software Engineer

Share background, conflicts, and proud project details

Share background, conflicts, and proud project details Behavioral & Leadership HR Screen — Software Engineer Context You are in an HR screen for a Sof...

Behavioral & Leadership
6
0
117 people solved
Aug 9, 2025
Databricks logo
Databricks
Hard
Software EngineerNew Grad Locked

Optimize a SQL query plan tree

This question evaluates understanding of relational algebra, query optimization techniques, and tree-based plan transformations such as predicate push...

Coding & Algorithms
7
0
65 people solved
Jan 22, 2026

Frequently Asked Questions

How difficult are Databricks interviews?
Databricks interviews are generally high bar and tend to be more difficult than average because the company evaluates both deep algorithmic skill and domain-specific data-platform knowledge. For Software Engineer roles you should expect LeetCode-style algorithmic problems at medium-to-hard difficulty plus systems and concurrency prompts drawn from real platform work like cache design, job schedulers, multi-threaded log writers, and lazy data structures. Data Scientist interviews mix classical statistics and practical data-manipulation problems (linear-regression diagnostics, ROC vs PR tradeoffs, nearest-neighbor similarity), while ML Engineer and Data Engineer loops emphasize production ML and pipeline debugging respectively. Strong communication and tradeoff reasoning are required throughout.
What does the Databricks interview process look like and which roles and categories appear most often?
Databricks commonly uses a multi-stage process: recruiter screen, one or two technical screens (coding or ML/statistics), and a virtual onsite containing 3–5 interviews (coding, system/architecture, and behavioral/hiring-manager rounds). Coding and system-design questions dominate for Software Engineer candidates and often reference distributed-data themes such as Spark-style computation, Delta Lake consistency, job orchestration and storage/compute tradeoffs. Data Scientist loops focus on hypothesis testing, regression diagnostics, and scalable SQL or similarity searches. Machine Learning Engineer and Data Engineer interviews center on productionizing models, OOM and harmful-content detection, and diagnosing pipeline performance and data-quality issues.
What is a practical prep timeline before interviewing at Databricks?
A focused 6-week plan tends to work well: weeks 1–2 refresh core algorithms, data structures, and coding speed with timed problems; weeks 3–4 study distributed-data concepts, Spark/Delta basics, and system-design patterns relevant to job schedulers, caching, and storage-consistency tradeoffs; week 5 concentrate on role-specific skills—statistics and evaluation metrics for Data Scientists, gradient-descent and model-deployment for MLEs, pipeline debugging for Data Engineers; week 6 run full mock interviews, practice clear tradeoff explanations, and rehearse STAR behavioral stories showing impact and ownership. Adjust intensity based on experience and time available.
What key subtopics should I focus on for Databricks interviews?
Prioritize a short list that maps to the company’s product focus and the question breakdown: for engineers, algorithms, concurrency, and storage-oriented system design problems such as dependency-aware job schedulers, cache designs with hit-count tracking, multi-threaded log writers, and lazy-array/KV-store implementations; for data science, linear-regression assumptions, multicollinearity diagnosis, hypothesis testing, ROC-AUC vs PR-AUC tradeoffs, and nearest-neighbor similarity across datasets; for ML engineers, solid numerical optimization (gradient descent) and production safety (OOM and harmful-content detection); for data engineers, pipeline performance troubleshooting, partitioning, and data-quality diagnostics.
Any standout interview tips and common pitfalls to avoid at Databricks?
Show systems thinking: explain consistency, fault modes, cost/latency tradeoffs, and operational concerns (retries, backpressure, monitoring) when designing solutions. In coding rounds write clean, testable code and discuss complexity and edge cases. For data and ML questions, be explicit about metric choices, assumptions, and how data quality affects conclusions. Avoid common pitfalls like ignoring distributed constraints, skipping concurrency details, failing to justify design tradeoffs, or overfitting to toy metrics. Close each answer with a short summary of tradeoffs and next steps you would take in production to demonstrate ownership and pragmatism.

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