dbt Interview Questions for Data Engineers

01

The 5 kinds of dbt question

Cut by what a question asks you to DO, not by the topic it names. The same topic scores differently when you have to explain it, build it, or work out why it broke — and those three fail separately.

• 20ConceptualWhat dbt is doing on your behalf. Why ref() exists rather than a table name, how the DAG is derived rather than declared, and what each materialization actually writes.Models & ref/source · DAG & lineage · Materializations (view/table/incremental/ephemeral/MV) · dbt Core vs Cloud→
• 20Model & Macro WritingWrite the model, or the macro that stops five models repeating themselves. Jinja is the part that goes wrong: the signal is knowing when a macro clarifies and when it hides the SQL somebody has to debug at 3am.Models & ref/source · Macros & Jinja · Materializations (view/table/incremental/ephemeral/MV) · Packages→
• 20Project Structure & ConventionsLay out a project from nothing: staging, intermediate and mart layers, naming that survives a hundred models, where tests live, and how environments and targets keep development off production.DAG & lineage · Environments, profiles & targets · Seeds & exposures · Docs→
• 20Debug & FixThe run failed, or worse, it passed and the numbers moved. A circular ref, a test that never had rows to fail on, an incremental model quietly missing yesterday because the filter reads the model it is building.Tests (generic, singular, dbt-utils, unit tests) · Source freshness · Snapshots & SCD2 · Incremental strategies (merge / insert_overwrite / append) & unique_key→
• 20Incremental & Cost OptimizationThe project runs for two hours and costs more than the warehouse it feeds. Choose the incremental strategy and the unique key, decide what full-refreshes and when, and cut CI down to what actually changed.Incremental strategies (merge / insert_overwrite / append) & unique_key · CI/CD & slim CI (state:modified) · Materializations (view/table/incremental/ephemeral/MV) · Semantic layer & metrics→

02

What dbt interviews actually cover

• 01Models & ref/source
• 02DAG & lineage
• 03Materializations (view/table/incremental/ephemeral/MV)
• 04Incremental strategies (merge / insert_overwrite / append) & unique_key
• 05Snapshots & SCD2
• 06Tests (generic, singular, dbt-utils, unit tests)
• 07Source freshness
• 08Macros & Jinja
• 09Packages
• 10Seeds & exposures
• 11Docs
• 12Environments, profiles & targets
• 13CI/CD & slim CI (state:modified)
• 14Semantic layer & metrics
• 15dbt Core vs Cloud

03

Learn it, then practise it