Lead Software Engineer (Databricks Platform Engineer)
Posted Aug 17, 2026 · We last checked this listing on Oct 5, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a reference architecture or engineering standard you created. How did you get teams to adopt it?
What they are checking: Driving platform adoption through developer experience and influence, not mandates.
Tell me about a platform outage or major incident you dealt with. What changed afterward?
What they are checking: Ownership under pressure and follow-through on reliability improvements.
How do you gather feedback from the teams using your platform, and how does it shape your roadmap?
What they are checking: Treating the platform as a product with users, which drives adoption and real developer experience gains.
What would you do
Databricks costs jumped 40 percent last month. How do you find out why and fix it?
What they are checking: Cost observability skills, from cluster policies to job tuning, and working with teams instead of blaming them.
A new Databricks feature looks promising but is still in preview. How do you decide whether to use it?
What they are checking: Evaluating emerging technology with discipline, weighing risk and support before betting the platform on it.
About the work
How would you design a Databricks platform so dozens of teams can self-serve without creating a governance mess?
What they are checking: Platform thinking around workspaces, Unity Catalog, templates and guardrails that make the right path the easy path.
Walk me through how you automate workspace, cluster and job provisioning. What tools have you used?
What they are checking: Infrastructure-as-code and CI/CD depth, since manual setup doesn't scale for an enterprise platform.
How do you decide what belongs in a shared platform service versus what each team should build themselves?
What they are checking: Judgment on reuse versus flexibility so the platform helps teams rather than slowing them.
How would you handle access control for sensitive healthcare data on the platform?
What they are checking: Practical knowledge of fine-grained permissions, row and column security and auditability for regulated data.
How do you support machine learning and AI workloads on the same platform as analytics and reporting?
What they are checking: Understanding of different compute, serving and governance needs across ML, AI and BI users.
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Practice this out loudThe full job description
As published by Vizient.