Databricks interview questions for engineers

CANDIDATE-REPORTED · INDEPENDENT SYNTHESIS

What candidates report about the interview

4 reviewed signals

Use these reports to choose what to rehearse, not to predict a fixed loop. Formats vary by role, team, and date; confirm your current process with the recruiter. Frontier Rehearsal is independent and not company-endorsed.

Process signalsReported stages, formats, timing, and role-specific variations.3

According to candidate reports, the Databricks solutions-architect loop has moved to an HR call, a hiring-manager conversation, a design-and-architecture session, a live vibe-coding session where the model writes most of the code and the candidate explains it, and a presentation.
Open permanent link
Candidates report that Databricks runs a technical screen and then a team-match step before the virtual onsite, and that this matching can stretch for weeks or months.
Open permanent link
Candidate reports describe a Databricks domain deep-dive and tech-fit round split into a project deep dive in the first half and hypothetical scenario questions in the second half.
Open permanent link

Candidate briefingsCompact, independently written summaries of fuller candidate timelines.1

According to a detailed candidate timeline, Databricks' process ran a recruiter screen, a system-design phone screen, a four-round virtual onsite, an additional coding round, reference checks with several managers and peers, and finally a hiring-committee decision roughly three months after the first call.
Open permanent link

Evidence reviewed Nov 12, 2026. Reports can become stale.

Question groups

Topics

Independent and not affiliated with the companies represented. Collected material does not predict future interview questions.

1 question

Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.

Free practice

Topic tags
Python
ML / AI

DatabricksCodingHalf AI-generated

Python codingWebAssembly + public tests

Reported interview periodApr 2026

Linear Regression with Batch Gradient Descent

Implement ordinary linear regression trained with full-batch gradient descent and mean squared error.

Practice focusAssumptions features is an n x d matrix with n = 1 and d = 1 , without a bias column. Return [intercept, coefficient 1, ..., coefficient d] . Initialize all weights to zero and round final values to six decimal places. Feature and target values and the learning rate must be finite numbers, not booleans; steps must be a nonnegative integer, not a boolean. Reject invalid shapes or parameters with ValueError . Do not mutate the supplied matrix or targets. The provided run case owns fixture dispatch, not candidate work.…

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CANDIDATE-REPORTED · INDEPENDENT SYNTHESIS

What candidates report about the interview

4 reviewed signals

Use these reports to choose what to rehearse, not to predict a fixed loop. Formats vary by role, team, and date; confirm your current process with the recruiter. Frontier Rehearsal is independent and not company-endorsed.

Process signalsReported stages, formats, timing, and role-specific variations.3

According to candidate reports, the Databricks solutions-architect loop has moved to an HR call, a hiring-manager conversation, a design-and-architecture session, a live vibe-coding session where the model writes most of the code and the candidate explains it, and a presentation.
Open permanent link
Candidates report that Databricks runs a technical screen and then a team-match step before the virtual onsite, and that this matching can stretch for weeks or months.
Open permanent link
Candidate reports describe a Databricks domain deep-dive and tech-fit round split into a project deep dive in the first half and hypothetical scenario questions in the second half.
Open permanent link

Candidate briefingsCompact, independently written summaries of fuller candidate timelines.1

According to a detailed candidate timeline, Databricks' process ran a recruiter screen, a system-design phone screen, a four-round virtual onsite, an additional coding round, reference checks with several managers and peers, and finally a hiring-committee decision roughly three months after the first call.
Open permanent link

Evidence reviewed Nov 12, 2026. Reports can become stale.

Question groups

Topics

Independent and not affiliated with the companies represented. Collected material does not predict future interview questions.

1 question

Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.

Free practice

Topic tags
Python
ML / AI

DatabricksCodingHalf AI-generated

Python codingWebAssembly + public tests

Reported interview periodApr 2026

Linear Regression with Batch Gradient Descent

Implement ordinary linear regression trained with full-batch gradient descent and mean squared error.

Practice focusAssumptions features is an n x d matrix with n = 1 and d = 1 , without a bias column. Return [intercept, coefficient 1, ..., coefficient d] . Initialize all weights to zero and round final values to six decimal places. Feature and target values and the learning rate must be finite numbers, not booleans; steps must be a nonnegative integer, not a boolean. Reject invalid shapes or parameters with ValueError . Do not mutate the supplied matrix or targets. The provided run case owns fixture dispatch, not candidate work.…

Databricks interview questions for engineering roles

Databricks interview questions for engineering roles

What candidates report about the interview

Linear Regression with Batch Gradient Descent

What candidates report about the interview

Linear Regression with Batch Gradient Descent