Palantir Data Engineer Interview Questions

I would define the metric grain, the deduplication rules, and the exact business logic before writing the SQL. Then I would validate the result against edge cases and compare it with known samples or prior reports. Trustworthy analytics depends on semantics being precise, not just the query being syntactically correct.

I would check upstream data shape, source freshness, schema drift, and changes in business rules or external systems. Sudden output changes often come from the data itself rather than the pipeline code. I would compare recent successful runs with the failing one to isolate what actually changed.

I would make lineage explicit by tracking source tables, transformation steps, and output dependencies. That helps when debugging incidents, answering governance questions, or understanding impact before changing something. In a production environment, traceability is what lets teams move fast without losing control.

I would break the query into stages and test each part independently with small sample data. Wrong results usually come from join logic, aggregation grain, null handling, or filtering mistakes rather than from the database engine itself. The best debugging approach is to identify the first step where the result diverges from expectation.

I would start with the business requirement and the user impact. If freshness is critical, speed may matter most; if the pipeline is expensive at scale, cost may need attention; if failures are frequent, reliability comes first. In practice, the right priority depends on which failure hurts the business most.