Palantir Data Engineer Interview Questions

I would explain what I would keep, what I would change, and why the original design stopped being the right fit. A strong answer shows that I can evaluate architecture honestly instead of defending past work just because it already exists.

I would keep the pipeline modular, document the data contracts clearly, and separate raw ingestion from transformation and serving layers. As systems grow, clarity matters as much as performance because teams need to reason about lineage, ownership, and failure modes quickly.

Batch processing handles data in chunks on a schedule, while streaming processes data continuously as it arrives. I would choose batch when latency is less important and simplicity matters more, and streaming when near-real-time behavior is required. Many production systems use both because they solve different problems.

I would add validation at ingestion, assert schema and volume expectations, and make the pipeline idempotent so reruns are safe. I would also keep explicit checkpoints and alerts so failures are visible before they affect downstream consumers. The goal is to make bad data hard to publish.

I would first define a canonical model so the system has one source of truth for key fields. Then I would map each source into that model, resolve conflicts deterministically, and keep provenance so the team can trace where disagreements came from. The answer is usually not to merge everything blindly, but to standardize intentionally.