DevOps Engineer Interview Experience at Databricks – 2026
Databricks
General SRE Pattern
No specific, verified real Databricks DevOps candidate account was found. Built from generally reported real DevOps loop patterns. This entry is a reconstruction, not a first-hand transcript.
Application Story
Candidates apply online and are typically contacted for a recruiter screen within a week, followed by a technical round and a system design round, with the hiring manager round scheduled last.
Preparation Journey
Preparation should focus on Kubernetes at scale and cloud infrastructure automation, since Databricks runs large multi-tenant Spark clusters on top of major cloud providers.
Why Selected / Rejected
Pending decision.
Resume Tips
For infrastructure roles at data platform companies, mention any experience running compute-heavy workloads (Spark, ML training jobs). Show knowledge of resource scheduling.
Telephonic
Recruiter screen on background and specific interest in infrastructure work at a company whose core product is compute-heavy, bursty Spark and ML workloads rather than a typical steady-state web service.
Technical
Covers Kubernetes resource management specifically for compute-heavy, bursty workloads - how to size resource requests versus limits when a job might need 2 CPUs most of the time but spike to 8 during a shuffle-heavy stage, and the risk of setting limits too conservatively (jobs get OOMKilled) versus too generously (poor cluster bin-packing and wasted capacity). The Terraform portion asks candidates to design module structure for provisioning per-team isolated environments with consistent guardrails (mandatory tagging, budget alerts) applied uniformly.
System Design
Candidates are asked to design infrastructure for safely running many isolated, resource-intensive workloads on shared Kubernetes clusters without one tenant's job starving another's. A strong approach proposes Kubernetes ResourceQuotas and LimitRanges scoped per namespace as the baseline guardrail, combined with priority classes so that latency-sensitive workloads can preempt lower-priority batch jobs under contention, and node pools segmented by workload profile (memory-optimized versus compute-optimized) rather than a single undifferentiated pool.
Questions Asked at Databricks in the DevOps Engineer Interview
Telephonic Round
Recruiter screen on background and specific interest in infrastructure work at a company whose core product is compute-heavy, bursty Spark and ML workloads rather than a typical steady-state web service.
Technical Round
Covers Kubernetes resource management specifically for compute-heavy, bursty workloads - how to size resource requests versus limits when a job might need 2 CPUs most of the time but spike to 8 during a shuffle-heavy stage, and the risk of setting limits too conservatively (jobs …
System Design Round
Candidates are asked to design infrastructure for safely running many isolated, resource-intensive workloads on shared Kubernetes clusters without one tenant's job starving another's. A strong approach proposes Kubernetes ResourceQuotas and LimitRanges scoped per namespace as the …
Prepare for this Role
At a Glance
DevOps Engineer at Other Companies
Have your own story?
Share it and help the next candidate prepare.
Comments
Loading comments...