Senior 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 reusable platform service or framework you built. How did you get teams to adopt it?
What they are checking: Whether they build things other engineers actually use and support them after release.
What would you do
Enterprise Architecture sets a standard you think will slow teams down. What do you do?
What they are checking: Whether they raise concerns constructively with evidence while respecting the architectural direction.
A data science team says the platform is too locked down to do their work. How do you respond?
What they are checking: Whether they can balance governance and productivity by finding safe ways to meet the team's need.
About the work
How would you set up Unity Catalog for an enterprise Databricks platform with many teams and sensitive healthcare data?
What they are checking: Whether they understand catalogs, permissions and governance well enough to secure data without blocking teams.
Walk me through how you've managed Databricks workspaces, clusters or jobs with Terraform or other Infrastructure-as-Code.
What they are checking: Whether they treat the platform as code so environments are repeatable and changes are reviewed.
What self-service capabilities would you build so data engineers can start projects without waiting on the platform team?
What they are checking: Whether they think about developer experience and paved paths that improve adoption.
How do you control Databricks costs as usage grows across the organization?
What they are checking: Whether they use cluster policies, monitoring and chargeback rather than just hoping usage stays reasonable.
How would you support machine learning and AI workloads on the platform alongside analytics jobs?
What they are checking: Whether they understand the different needs of ML, like GPU compute, model serving and feature management.
How do you roll out a Databricks runtime upgrade without breaking production jobs?
What they are checking: Whether they plan upgrades with testing, communication and rollback rather than flipping a switch.
How do you set up CI/CD for notebooks and jobs on Databricks?
What they are checking: Whether they bring software engineering discipline to platform and data code.
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As published by Vizient.