Snowflake data interview guide
Warehouse-centric SQL — semi-structured data, performance and modeling.
typical interview loop
- 1.Recruiter screen
- 2.Technical screen: SQL plus a Python data-manipulation exercise
- 3.Onsite: data modeling / pipeline design, coding, behavioral
- › Know QUALIFY-style filtering via window functions.
- › Discuss clustering and query cost.
most-tested topics
filtering
4
JOIN
3
Window functions
3
GROUP BY
2
groupby
2
simulation
1
Based on 16 reconstructed Snowflake-style questions.
practice path
0 free · 16 Pro- 1Active shares per databasesqlMedium
- 2Auto-suspend savingspythonMedium
- 3Average scan size per warehouse sizesqlMedium
- 4Credit efficiency per warehousesqlMedium
- 5Credits per warehousesqlEasy
- 6Credits with cumulative sharepandasHard
- 7Day-over-day credit changesqlHard
- 8Detect query gaps and islandspythonHard
- 9Failed queries by usersqlEasy
- 10Poorly clustered tablessqlMedium
- 11Queries that spilled to remote storagesqlMedium
- 12Scan efficiency per warehousepandasMedium
- 13Share of credits per warehousesqlHard
- 14Slowest query per usersqlHard
- 15Spill rate by userpandasMedium
- 16Warehouse size orderingpythonEasy
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