Databricks · Technology

Databricks DevOps Engineer Interview

Complete guide to the DevOps Engineer interview at Databricks — real questions, insider tips, salary data, and stage-by-stage preparation.

2–3 weeks from first contact to offer

4 stages

14 questions

Practise this interview free

Overview

Interviewing for DevOps Engineer at Databricks

Interviewing for a DevOps Engineer position at Databricks is a distinct experience from applying to the same role elsewhere. Databricks with 2,800+ employees, has built a structured hiring process that reflects both the demands of the DevOps Engineer role and the company's own values and culture. The process is designed to assess not just whether you can do the job technically, but whether you'll thrive in Databricks's specific working environment.

For DevOps Engineers specifically, Databricks tends to emphasise practical problem-solving and technical depth alongside cultural fit. You should expect a process that tests your ability to work with tools like Kubernetes and Docker, CI/CD platforms (Jenkins, GitLab, GitHub Actions), Infrastructure-as-Code (Terraform, Ansible) in realistic scenarios, not just abstract theory. The interviewers are typically people you'd be working with directly, so the conversation goes both ways — they're evaluating you, but you're also getting a genuine sense of the team and day-to-day work.

Understanding what Databricks values — and how that translates into their interview expectations for a DevOps Engineer — gives you a significant advantage. This guide breaks down the full process, the specific questions you're likely to face, and how to prepare effectively.

Process

How Databricks interviews DevOps Engineers

Databricks's interview process for DevOps Engineer roles typically runs 2–3 weeks and involves 4 distinct stages. The process begins with recruiter screen and progresses through increasingly focused assessments. Each stage is designed to evaluate different aspects of your suitability — from baseline qualifications through to cultural alignment and role-specific capability.

For DevOps Engineer candidates specifically, expect the technical stages to focus on your hands-on ability with Kubernetes and Docker, CI/CD platforms (Jenkins, GitLab, GitHub Actions), Infrastructure-as-Code (Terraform, Ansible), Monitoring and logging (Prometheus, ELK, Datadog). Databricks typically includes a practical assessment — this could be a coding challenge, a system design discussion, or a technical case study depending on the seniority level. The behavioural stages will probe your collaboration style and how you handle ambiguity, since DevOps Engineers at Databricks work across teams regularly.

Recruiter Screen

Initial conversation about background and interest in Databricks.

Tailor your application specifically for the DevOps Engineer role at Databricks. Highlight experience with Kubernetes and Docker, CI/CD platforms (Jenkins, GitLab, GitHub Actions), Infrastructure-as-Code (Terraform, Ansible) and use language that mirrors their job description. Databricks receives high volumes of applications, so a generic CV will be filtered out.

Technical Phone Interview

Coding or system design. Expect data engineering or distributed systems questions.

Prepare concrete examples of your DevOps Engineer work. Be ready to solve problems live — talk through your reasoning, consider edge cases, and demonstrate how you'd use Kubernetes and Docker and CI/CD platforms (Jenkins, GitLab, GitHub Actions).

On-site Interviews (2–3 rounds)

Technical interviews covering coding, system design, and data engineering. Assess depth and fit.

Research Databricks's approach to this stage. Prepare specific examples from your DevOps Engineer experience that demonstrate the qualities they value: data engineering passion, technical depth, scale thinking.

Manager Round

Conversation with hiring manager about role and team.

Format

Interview format and logistics

As a mid-size organisation, Databricks's interview process for DevOps Engineer roles tends to be more personal and direct than at larger employers. Expect fewer formal stages — typically 2-3 rounds rather than 4-5 — with earlier access to the hiring manager or team lead. Interviews may be conducted via video call or in person depending on location. The format is less rigidly structured than at enterprise companies, which means you'll have more opportunity for genuine conversation, but the expectations are equally high. Come prepared to discuss your experience in depth rather than delivering polished, rehearsed answers.

Qualities

What Databricks looks for in DevOps Engineers

Data Engineering Passion

Databricks values data engineering passion because Genuine interest in data systems and analytics. Databricks solves data problems; you need to care about data excellence..

For the DevOps Engineer role, show this by sharing examples where you used Kubernetes and Docker or CI/CD platforms (Jenkins, GitLab, GitHub Actions) to deliver measurable results.

Technical Depth

Databricks values technical depth because Strong fundamentals and problem-solving. Distributed data systems are complex; you need deep knowledge..

For the DevOps Engineer role, show this by sharing examples where you used Kubernetes and Docker or CI/CD platforms (Jenkins, GitLab, GitHub Actions) to deliver measurable results.

Scale Thinking

Databricks values scale thinking because Comfort designing systems handling massive data volumes. Performance and efficiency matter deeply..

For the DevOps Engineer role, show this by sharing examples where you used Kubernetes and Docker or CI/CD platforms (Jenkins, GitLab, GitHub Actions) to deliver measurable results.

Customer Focus

Databricks values customer focus because Understanding of customer problems and willingness to learn about data workflows..

For the DevOps Engineer role, show this by sharing examples where you used Kubernetes and Docker or CI/CD platforms (Jenkins, GitLab, GitHub Actions) to deliver measurable results.

Reliability mindset

For DevOps Engineer roles specifically, reliability mindset is essential because Do you naturally think about failure modes? Can you explain graceful degradation, rollback strategies, and disaster recovery?.

Prepare 2-3 examples from your experience that clearly demonstrate reliability mindset. Databricks's interviewers will probe this in behavioural questions.

Questions