Databricks Data Engineer candidates report recruiter or manager screens, SQL or coding work, Spark-focused technical discussions, architecture and project deep dives, and later behavioral or executive conversations.
$195K
Avg. Base Comp
$375K
Avg. Total Comp
4-7 rounds
Typical Rounds
2 months
Process Length
Databricks Data Engineer interviews reported here place substantial weight on practical data-platform reasoning. Spark is the clearest recurring technical theme: candidates describe questions about RDDs versus DataFrames, Spark versions, cloud architecture, and Databricks-oriented solution design. Several accounts also include SQL work, such as multiple joins and window functions, while one candidate could choose SQL or another programming language for a CodeSignal assessment.
Preparation should connect those topics to work you have actually delivered. Candidates report detailed discussions of previous internships, project delivery, architecture, and technical knowledge. Practice explaining the purpose of a pipeline or system, the constraints you faced, the choices you made, and how you would communicate the design to architecture or subject-matter stakeholders. One accepted candidate also encountered two algorithm interviews, including a job-dependency problem using topological sorting, so review how to represent dependencies and explain graph reasoning clearly.
Behavioral preparation matters as well. Reported manager, panel, and executive conversations covered prior responsibilities, motivation for Databricks, company values, constructive feedback, and project experience. Bring concise examples that connect technical judgment with ownership and communication.
The sequence varies considerably across accounts. Some candidates report take-home work, panels, or executive conversations, while another process ended during the initial recruiter call. Clarify the required Spark depth, assignment scope, expected stages, and likely time commitment before investing in later rounds.
Synthesized from 9 candidate reports by our editorial team.
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Real interview reports from people who went through the Databricks process.
The process ended much earlier than I expected: the recruiter stopped the call around the 15-minute mark and said they would not be moving forward. It was only an initial recruiter conversation, but it felt more like an abrupt technical screen. I had been invited based on my CV, where I had already been clear that I did not have deep Spark experience. During the call, though, it became clear that deep Spark expertise was the most important requirement, even though that had not been stated in the job ad.
The recruiter asked me to explain what an ETL pipeline is, then moved into Spark-specific questions, including the difference between RDDs and DataFrames and a question about Spark versioning and the differences between versions. A few questions were rephrased or corrected several times while being asked, which made it harder to understand what they were looking for. The tone came across as annoyed and unfriendly throughout. After telling me I was not a fit, the recruiter still let me ask questions. When I asked what the broader interview process would have looked like, I felt judged for asking despite being told we might stay in touch about other roles.
My main takeaway is to confirm the expected depth of Spark knowledge before accepting an initial conversation, especially if the listing only mentions it generally. Be ready to clearly explain ETL fundamentals, RDDs versus DataFrames, and Spark-version differences, but also make sure the role actually matches the level of Spark experience on your resume.
Prep tip from this candidate
Before the recruiter screen, verify whether the team requires deep hands-on Spark expertise. Prepare concise explanations of ETL pipelines, RDDs versus DataFrames, and Spark version differences.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Databricks
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Monthly Customer Report | |
| Total Spent on Products | |
| String Mapping | |
| Hurdles In Data Projects | |
| Centralized Event Ingestion | |
| Cumulative Sales By Product | |
| Target Indices | |
| Priority Queue Using Linked List | |
| Transformer Encoder Layer | |
| Possibly Biased Coin | |
| Shortest Path Algorithms | |
| Text Editor With OOP | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Weighted Average With Missing Dates | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Top Three Salaries | |
| Experiment Validity | |
| String Shift | |
| Last Transaction | |
| Top 3 Users | |
| Third Purchase | |
| First Touch Attribution | |
| Minimum Change | |
| Find the First Non-Repeating Character in a String | |
| Daily Retention Summary | |
| RMS Error | |
| Sort Strings |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an early recruiter or hiring-manager discussion covering background, current role, motivation, preferences, logistics, and sometimes an initial check of Spark knowledge. One account ended during this screen after ETL and Spark questions, so clarify the expected hands-on depth early.
Candidates report different assessment formats: a CodeSignal exercise with a choice of SQL or another language, a two-question coding assignment, and take-homes using SQL and PySpark transformations and aggregations. Some describe joins, window functions, optimized code, and edge cases.
Technical interviews may be conversational rather than a single coding problem. Candidates report rapid questions on Spark, SQL, databases, big-data technologies, RDDs versus DataFrames, Spark internals, versioning, and how Spark scales with increasing data volume.
Candidates report architecture discussions based on realistic customer scenarios, plus deep dives into prior projects. Be ready to explain an end-to-end pipeline or platform, tradeoffs, cloud components, CI/CD, Git workflows, optimization choices, and the problems you solved.
Later stages may include a broader technical and competency panel, director or VP conversations, or an executive-fit discussion. Candidates report questions about ownership, ambiguity, difficult client requests, prior feedback, company motivation, and communicating data topics to non-technical stakeholders.
