Infrastructure as Code & Cloud Architecture
This area tests your ability to design, provision, and manage cloud infrastructure reliably and at scale. At Databricks, infrastructure is entirely codified. Interviewers evaluate your proficiency with Terraform, your understanding of state management, and your grasp of cloud-native networking (VPCs, subnets, routing, IAM). Strong performance means you can design a secure, highly available architecture while clearly articulating the trade-offs between different cloud services.
Be ready to go over:
- Terraform state and modules – Handling state locking, remote backends, and writing reusable infrastructure code.
- Kubernetes architecture – Understanding the control plane, data plane, ingress controllers, and pod networking.
- Cloud networking & security – Designing secure boundaries, managing IAM roles, and implementing least-privilege access.
- Advanced concepts – Multi-region disaster recovery, service mesh implementation, and Kubernetes operators.
Example questions or scenarios:
- "Design a highly available Kubernetes cluster across multiple availability zones using Terraform."
- "How would you handle a situation where your Terraform state file becomes corrupted or out of sync?"
- "Walk me through the network path of a request hitting a service inside a Kubernetes cluster."
Coding & Automation
DevOps Engineers at Databricks write real software to solve operational problems. This is not a standard LeetCode algorithms interview, but rather a test of your ability to write functional, production-ready code. You will be evaluated on your scripting abilities, error handling, and how you interact with APIs. Strong candidates write clean code, handle edge cases gracefully, and write tests for their logic.
Be ready to go over:
- API integration – Writing scripts to interact with REST APIs, handling pagination, and managing rate limits.
- Data parsing and manipulation – Reading logs, filtering JSON/CSV data, and aggregating metrics.
- Concurrency – Basic multithreading or multiprocessing in Python or Go to speed up operational tasks.
- Advanced concepts – Writing custom Kubernetes controllers or developing internal CLI tools.
Example questions or scenarios:
- "Write a Python script to parse a massive web server log file and output the top 10 IP addresses with the most 5xx errors."
- "Create a tool that queries the AWS API to find all unattached EBS volumes and safely deletes them."
- "Implement a function to deploy a configuration file to multiple servers concurrently, reporting any failures."
System Troubleshooting & Linux Internals
Systems fail, and your ability to diagnose and remediate those failures is critical. Interviewers will present you with a broken system scenario and evaluate your methodology. Strong performance involves a systematic, top-down or bottom-up approach to isolation, rather than randomly guessing commands. You must demonstrate a deep understanding of the Linux kernel, networking stack, and resource limits.
Be ready to go over:
- System observability – Using tools like
top, strace, lsof, tcpdump, and iostat to diagnose performance bottlenecks.
- Network troubleshooting – Debugging DNS resolution, TCP handshakes, and routing issues.
- Resource exhaustion – Identifying memory leaks, CPU spikes, and inode exhaustion.
- Advanced concepts – eBPF for tracing, kernel panic analysis, and deep container isolation mechanics (cgroups/namespaces).
Example questions or scenarios:
- "A microservice is suddenly experiencing high latency. Walk me through exactly how you would investigate this from the ground up."
- "You cannot SSH into a Linux machine, but it responds to pings. What could be the issue, and how do you fix it?"
- "How do you trace a process that is consuming 100% CPU but isn't writing anything to standard application logs?"
