Software Engineer Interview Prep
Prep for Databricks' engineering loop - distributed data systems, strong coding fundamentals, and deep technical depth on data infrastructure.
About this loop
Databricks builds the infrastructure that powers data and AI workloads at scale - Delta Lake, Apache Spark, MLflow, Unity Catalog - and their interview process reflects what it takes to build those systems. Coding rounds are rigorous, skewing Medium-to-Hard with an emphasis on algorithmic correctness and clean implementation. System design rounds are data-systems flavored: designing distributed storage engines, query planners, streaming pipelines, and data lake architectures. Candidates who know how Spark's execution model works, why Delta Lake's transaction log is designed the way it is, or how distributed query execution handles skew have a genuine edge in design conversations. Behavioral rounds are lighter than at Amazon, but Databricks screens for technical depth and the ability to go very deep on a problem - they want engineers who can own a complex distributed system end to end, not just ship features.
The interview loop
- 1Recruiter screen30 minutes. Background, team alignment (Spark runtime, Delta Lake, MLflow, platform), level calibration. Databricks engineers often specialize - ask about the team's system and tech stack.
- 2Technical phone screen60 minutes. One to two coding problems. Algorithms and data structures, Medium difficulty. Some interviewers include a data-systems question to gauge background.
- 3Onsite: Coding round 160 minutes. Algorithmic problems, Medium-to-Hard. Correctness, edge cases, and clean code all evaluated. Graph, tree, and sliding window problems are common.
- 4Onsite: Coding round 260 minutes. Often more applied - may involve simulating a simplified distributed operation, designing a data structure for a specific access pattern, or implementing a core algorithm from data systems (e.g., merge logic for log-structured files).
- 5Onsite: System design60-75 minutes. Distributed data systems design: build a distributed key-value store, design a write-ahead log, architect a streaming pipeline, or design a simplified query execution engine. Depth on distributed storage, consistency, and fault tolerance is expected.
- 6Onsite: Behavioral / technical discussion45 minutes. Technical depth probe - they may ask you to walk through a complex system you've built, discuss how you'd debug a distributed performance issue, or reason through an open-ended data systems problem. Lighter than Amazon's LP round but still substantive.
What Databricks actually evaluates
- →Deep distributed systems knowledge - not just the buzzwords but the mechanisms: consensus, replication, partitioning, fault tolerance
- →Data engineering fundamentals - understanding how query engines, storage formats, and streaming systems actually work
- →Technical ownership - ability to own a complex system end to end, including the operational details
- →Algorithmic rigor - correct, clean code with edge cases handled and complexity analyzed
- →Open source mindset - Databricks is built on and contributes to open source; curiosity about how systems are built matters
- →Going deep - Databricks respects candidates who can go from 'what' to 'why' to 'how it actually works under the hood'
Topics tested
System Design
Data-systems flavored. Practice designing distributed storage (write-ahead logs, LSM trees, column stores), streaming pipelines (exactly-once delivery, watermarking, state management), and query execution engines. Knowing how Delta Lake or Spark actually work gives you concrete vocabulary for these discussions.
Algorithms
Medium-to-Hard across two rounds. External sort, merge algorithms, interval problems, and graph traversal appear regularly - all relevant to how data systems actually process large datasets.
Databases
This is unusually important at Databricks compared to most SWE roles. Storage formats (Parquet, ORC, Delta), transaction models (MVCC, write-ahead logging), query optimization (predicate pushdown, partition pruning), and consistency levels are all fair game.
Data Structures
LSM trees, B-trees, skip lists, and bloom filters come up in both design and coding rounds in the context of data systems. Know why you'd choose each.
Behavioral
Lighter than at Amazon but focused on technical depth and ownership. Prepare stories about complex systems you've built or debugged, and be ready to go deep on the technical details of your past work.
Operating Systems
Memory management, file I/O, and process scheduling surface in discussions about query engine performance and resource management. Useful background for the system design round.
Curated practice questions
346 MCQs and 100 coding challenges, grouped by topic. Free preview shows question titles - premium unlocks full content.
System Design · 68 MCQs
Browse all in System Design →Algorithms · 77 MCQs
Browse all in Algorithms →Databases · 49 MCQs
Browse all in Databases →Data Structures · 44 MCQs
Browse all in Data Structures →Behavioral · 63 MCQs
Browse all in Behavioral →Operating Systems · 45 MCQs
Browse all in Operating Systems →Algorithms - Coding challenges · 71 challenges
Browse all coding challenges →Data Structures - Coding challenges · 29 challenges
Browse all coding challenges →Practice in mock interview format
Behavioral and system design rounds reward practice with a live AI interviewer that probes follow-ups, not silent reading.
Start an AI mock interview →Frequently asked questions
Do I need to know Apache Spark to interview at Databricks?
Not required, but familiarity helps in system design rounds. If you know how Spark's execution model works - DAG scheduling, shuffle operations, the difference between transformations and actions, how data is partitioned and moved - you'll have concrete vocabulary for design discussions that other candidates won't. If you don't know Spark, understand the general problem it solves: distributed in-memory processing of large datasets, and the tradeoffs versus MapReduce or streaming systems.
What makes Databricks system design rounds different from standard FAANG rounds?
The domain. Standard FAANG design rounds favor web-system problems (news feeds, URL shorteners, chat). Databricks designs around data infrastructure: how do you store a petabyte-scale table with ACID guarantees, how does a distributed query engine handle a skewed join, how do you build an exactly-once streaming pipeline. Candidates who study generic system design but ignore distributed data systems will find these rounds harder than expected.
What is Delta Lake and why does it come up in interviews?
Delta Lake is Databricks' open source transactional storage layer that adds ACID guarantees to cloud object storage (S3, GCS, ADLS). It uses a write-ahead log (the Delta Log) to track all changes to a table. It comes up in interviews because it's a concrete example of the problems Databricks engineers work on: how do you implement transactions on an eventually consistent storage system, how do you handle concurrent writes, how does time travel work. You don't need to know the codebase, but understanding the design motivation is valuable.
How important is open source contribution?
It's a signal but not a requirement. Databricks engineers contribute heavily to Apache Spark, Delta Lake, MLflow, and other open source projects. Candidates who have contributed to relevant open source projects - or who can speak knowledgeably about how they work - stand out. If you haven't contributed, study the architecture of one project (Spark is well-documented) at the level where you could discuss design decisions.
What is the technical depth probe in the behavioral round?
Unlike Amazon's Leadership Principles round, Databricks uses the behavioral slot partly to evaluate technical depth. Expect questions like: 'walk me through the most complex distributed system you've built,' 'describe a hard debugging problem in a distributed environment and how you solved it,' or 'what are the tradeoffs in the design of a system you've worked on.' They're evaluating whether you understand your own systems deeply, not just whether you shipped something.
How does Databricks compare to Snowflake as an interview target?
Both are data infrastructure companies with high technical bars. Databricks skews toward open source, Spark-native, and lakehouse architecture; Snowflake skews toward managed cloud data warehouse and SQL-first. The interview processes are similar in rigor. Databricks values distributed systems depth; Snowflake values database internals depth (query optimization, columnar execution, storage). If you have strong Spark/Delta background, Databricks is a natural fit; if you have strong database internals background, both are good targets.