Palantir Data Engineer Interview Questions

I would make transformations composable, parameterized, and easy to test on sample inputs. Reusability should not create opaque logic, so I would still keep the business meaning visible and avoid hiding too much behavior in generic utilities. Good reuse reduces duplication without reducing clarity.

I would partition the data in a way that makes historical recomputation safe and deterministic. Then I would make the backfill process explicit, observable, and reversible so it can replace affected outputs without disturbing unaffected ones. Backfills are a normal part of data engineering, so they should be treated as first-class workflows.

I would make sure rerunning the same input produces the same final state. That usually means using stable keys, merge logic, atomic writes, and explicit checkpoints or deduplication where needed. Idempotency matters because retries and reprocessing are unavoidable in real systems.

I would separate the raw and curated layers, then publish clean, documented outputs for each audience. Analysts may need flexible historical data, while operational users need freshness and correctness guarantees. The architecture should support both without forcing one group to compromise the other.