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About this interview
A technical interview on Data Warehousing and Analytics Engineering, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain the difference between a data lake, data warehouse, and lakehouse and appropriate use cases for each
Describe how columnar storage and partitioning/clustering improve analytical query performance
Explain the analytics engineering workflow: sources, staging, intermediate, and mart layers
Describe how dbt manages lineage, testing, and documentation in a warehouse transformation layer
Topics covered
Data Storage ParadigmsColumnar Storage and Query OptimizationAnalytics Engineering WorkflowDimensional Modelingdbt Fundamentalsdbt Testing and Data Qualitydbt Lineage and Documentationdbt Project Design
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through the difference between a data lake and a data warehouse? What kinds of workloads belong in each?
“What is dimensional modeling, and why is it a common approach for structuring analytical data? Who popularized it?
“dbt v1.8 introduced unit tests. How do they work, and how are they different from the generic and singular test types that existed before?