Senior Solution Architect
Posted Sep 24, 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 technology or vendor evaluation you led. How did you reach a recommendation?
What they are checking: Whether they run fair proofs of concept with clear criteria rather than picking a favorite and justifying it.
Tell me about an architecture you designed that had to change after it was built. What did you learn?
What they are checking: Whether they can admit design mistakes and adapt their approach for future work.
What would you do
A product team wants to build a solution that doesn't follow the architectural standards. What do you do?
What they are checking: Whether they can enforce standards pragmatically and know when an exception is justified.
How do you explain an architectural tradeoff to business leaders who don't have a technical background?
What they are checking: Whether they can translate technical choices into cost, risk and speed terms leaders can decide on.
About the work
Walk me through a lakehouse architecture you'd design on Databricks to combine clinical, spend and operational data.
What they are checking: Whether they can design layers, domains and governance that integrate very different healthcare data sources.
How would you use Delta Lake and Unity Catalog to handle lineage, access and data quality?
What they are checking: Whether they understand how platform features support governance rather than treating it as separate work.
How would you architect a RAG solution over Vizient's data so it gives accurate, permission-aware answers?
What they are checking: Whether they handle retrieval quality, embeddings, access control and evaluation in generative AI design.
How do you design a semantic layer so different products report the same metric the same way?
What they are checking: Whether they understand that consistent definitions matter as much as pipelines for trustworthy analytics.
How do you plan for business continuity and disaster recovery in a cloud data platform?
What they are checking: Whether they consider recovery objectives and failure modes, not just the happy path.
How would you design observability for data pipelines and AI services across the platform?
What they are checking: Whether they plan for monitoring data freshness, quality and model behavior, not just system uptime.
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Practice this out loudThe full job description
As published by Vizient.