Data Warehouse Administrator Interview Questions for AI Training Work

I use a dimensional model with clearly defined fact and dimension tables suited to the actual reporting needs, rather than either an overly complex schema trying to anticipate every possible future need or an overly simple one that can't support real reporting requirements. I keep the schema aligned to what's actually being queried.

I design loads to be idempotent and use staging tables so a partial failure can be identified and reprocessed cleanly, rather than loading directly into production tables where a partial failure leaves the data in an inconsistent state. I build in validation checks before committing a load as final.

I build monitoring that flags unexpected schema changes early, like a new or missing column, rather than letting the pipeline fail silently or load incorrect data. I coordinate with source system owners about planned changes where possible, so the ETL process can be updated proactively rather than reactively.

I look at actual query execution plans to identify the specific bottleneck, like missing indexes or inefficient joins, rather than applying generic optimizations without diagnosing the real cause. I also evaluate whether partitioning or aggregation strategies could reduce the volume being scanned for the most common query patterns.

I build validation checks into the ETL pipeline to catch and flag data quality issues at ingestion rather than letting bad data flow silently into the warehouse, and I escalate patterns of source data quality problems to the source system owners rather than just cleaning them downstream indefinitely.

I'd assess whether a partial or previous day's data can serve as a reasonable stopgap while I diagnose the failure, communicating the situation clearly to affected users rather than leaving them to discover stale or missing data on their own, then fix the underlying issue and reprocess the load properly.

I'd trace the discrepancy back through the ETL transformation logic step by step rather than assuming the warehouse is simply wrong, since the mismatch could come from a legitimate transformation difference, like a different aggregation level, rather than an actual bug. I'd communicate the finding clearly either way to restore confidence.

I'd prioritize documenting and cleaning up the tables that are actually in active use first, rather than trying to fully standardize everything at once, since the ones being queried regularly carry the most risk if left undocumented, while unused legacy tables can be addressed or deprecated on a slower timeline.

Reports were running slowly, and the initial assumption was that the warehouse needed more hardware resources. Looking at the actual query plans showed a specific join pattern that had become inefficient as one table grew much larger than expected. Adding a targeted index resolved the performance issue without needing additional infrastructure.

A source system migrated to a new database platform with a different schema structure, which broke the existing extraction logic. I redesigned the extraction and transformation steps to work with the new source structure, testing thoroughly against the old and new outputs in parallel before cutting over, to make sure the warehouse data stayed consistent through the transition.