Databricks Data Engineer interviews in candidate reports combine recruiter and hiring-manager conversations, SQL tests or take-home assignments, Spark and architecture discussions, and panel or executive rounds. Formats vary widely between candidates.
$175K
Avg. Base Comp
$280K
Avg. Total Comp
4-7 rounds
Typical Rounds
Not reported
Process Length
Databricks Data Engineer interviews in these reports lean heavily on Spark, even when the job ad barely mentions it. One candidate's recruiter call ended about 15 minutes in after questions on ETL pipelines, RDDs versus DataFrames and differences between Spark versions. Another described Spark and Databricks-style solution design as the core of their technical discussions, so be ready to explain Spark fundamentals clearly before the first call.
The assessment format differs by report. One candidate faced a structured SQL test with multiple joins and window functions, along with the possibility of a take-home exercise in SQL or Python. Others completed a take-home coding assignment or a multi-day assignment done inside Databricks. One candidate also got a topological-sorting question about job dependencies in an algorithms round. Prepare for multi-part SQL problems first, and keep one graph or dependency problem in reserve.
Later rounds lean toward architecture and communication. Reports include a technical deep dive on Spark, cloud and architecture, a project-delivery discussion, an architecture and subject-matter-expert panel, and executive conversations. Practice explaining how you have delivered projects and defending your design choices out loud.
Behavioral questions appear alongside technical ones. Expect why Databricks, which one candidate heard even in otherwise technical rounds, along with negative feedback you have received and company values. One candidate also faced culture-fit questions where interviewers seemed to want very specific answers. Two late-stage candidates received generic rejections after long processes, so ask early about the remaining stages, assignment scope and timeline.
Synthesized from 13 candidate reports by our editorial team.
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Real interview reports from people who went through the Databricks process.
The round that decided everything for me was the panel presentation, and I'd tell anyone prepping for Databricks's data engineer loop to treat it that way. A few days before the loop, I got a fake customer scenario, and my job was to pitch a lakehouse migration to the panel. They didn't sit quietly through it. They interrupted me mid-sentence, sometimes acting like a skeptical CTO and sometimes like a defensive data engineer, to see how I handled pushback.
Before that, the process opened with a basic HR chat, then a tech screen that was mostly Python data manipulation and SQL window functions. The final loop is four rounds and honestly pretty exhausting. After the panel came the architecture deep dive, which is where you can't bluff. They went into Spark internals, including managing data skew, broadcast joins, and how the Delta Lake transaction log actually works. Then there was a live ETL coding round, and the last round was behavioral, mostly about working with core technical teams and sales teams.
For prep, I watched a lot of Databricks Summit videos on YouTube to pick up their terminology, which helped, but the panel was impossible to rehearse alone. I finally got a mock session with a real solutions architect who roleplayed the skeptical customer very convincingly. His main feedback was that my pitch was way too technical for a CTO persona, which was exactly what I needed to hear before the real thing. Going in, I'd say a dry run of the presentation is worth more than extra reading.
I accepted the offer.
Prep tip from this candidate
Run a full dry run of the panel pitch with someone who can play a skeptical CTO and interrupt you, since keeping the pitch at a business level was the main thing that needed fixing. Also drill Spark internals like skew handling, broadcast joins, and the Delta Lake transaction log at depth, because the architecture deep dive can't be faked.
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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 opening recruiter or HR call covering background, interest in the role, preferences and logistics, sometimes with an overview of the remaining process. One candidate was asked to explain ETL pipelines, RDDs versus DataFrames and Spark version differences, and the call ended early. Spark fundamentals are worth reviewing before this call.
Several candidates describe a hiring-manager conversation. One manager call included a detailed internship deep dive, negative feedback received, and what the candidate knew about Databricks and its values. Another candidate was asked why they wanted Databricks repeatedly across rounds. Prepare a candid, specific account of past work and a clear reason for wanting Databricks.
The assessment format varies by candidate. Reports mention a structured SQL test with multiple joins and window functions, a possible take-home in SQL or Python, a take-home technical coding assignment, and a multi-day assignment completed in Databricks. One candidate had two algorithm rounds, including topological sorting for job dependencies.
Candidates report technical discussions on Spark, cloud and architecture, and on Spark and Databricks-style solution design. One candidate had three 30-minute technical discussions. Another spoke with a Senior Data Engineer about their experience and technical knowledge. Expect to explain your reasoning rather than stay at diagram level.
Later stages reported by candidates include a panel combining technical and competency-based questions, a project-delivery discussion, an architecture and subject-matter-expert panel, culture-fit conversations and an executive round. Some candidates reported these stages ending without an offer, and one said an expected final wrap-up never happened.
