HackerRank 60–90 min: coding + SQL + sometimes API integration
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Big Tech & Enterprise Software
The data role is Forward Deployed Software Engineer: practical coding, decomposition/debugging, data architecture, and a hard mission filter.
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And what its data engineers work with.
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Stages in the order candidates meet them.
5 stages. 4 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
2 reported questions. See them below
CodePair + embedded behavioral
Medium confidence2 reported questions. See them below
2 reported questions. See them below
1 reported question. See them below
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By round. Questions marked Expected are written from the company's standards.
Online AssessmentMedium confidence
CodingMedium confidence
Decomposition / DesignMedium confidence
Re-engineering / DebuggingMedium confidence
Nobody reported these exact questions at Palantir. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.
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Ranked by how much of the evidence points at each topic.
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Do the first group before the second.
Practical coding (DP/graphs/trees/hashing)
Do first (4)Decomposition design (chess/parking-style)
Debugging long codebases; Python+SQL data pipelining
Articulate mission alignment
Data architecture for client problems
ThenBehavioral/social competence
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Why candidates were turned down, as reported.
Weak mission/culture alignment (filtered early)
Behavioral is named in 7 of 50 company accounts.Abstract over practical coding
Python is named in 13 of 50 company accounts.Cannot debug long real code
Low social competence (FDSE is client-facing)
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3 reported, 2 composite. Each opens and closes. Composites are labelled.
Not a data engineering account. A forward deployed software engineer in Denver (March 2025) accepted an offer after a recruiter call, an online test, an onsite (coding, breaking a problem down, debugging, architecture) and a hiring manager round. They called the experience positive.
What to take from it Palantir tests how you break a problem down.
Not a data engineering account. A forward deployed engineer in the US (November 2025) went through the same loop and got no offer. They had a negative view. It is useful for what goes wrong: fit with the mission and practical coding.
What to take from it Show fit with the mission and write practical code.
Not a data engineering account. In a problem-breakdown round the candidate had to design Yelp, going back and forth with the interviewer. In a learning round they got the documentation of an obscure programming language and had to solve problems. They say that asking questions that sound like you need hand-holding can hurt.
What to take from it Learn fast from documentation, and ask sharp questions.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
This is a composite, not one person's account. It is built from the loop we have on record for Palantir. Questions reported for this round: HackerRank 60–90 min: coding + SQL + sometimes API integration; Implementation-heavy mini-projects.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for Palantir. CodePair + embedded behavioral. Questions reported for this round: DS&algo medium–hard (DP, graphs, trees, arrays, hash tables); Practical motivations over abstract puzzles.
What to take from it Practise these questions before you reach this round.
Palantir's data role is usually FDSE, not "Data Engineer." Mission fit gates the whole loop.
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Their reported questions, one at a time, against a stopwatch.
HackerRank 60–90 min: coding + SQL + sometimes API integration
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Palantir asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.
Pipeline and system design
Read: Data pipeline chaptersDrill: Pipeline practiceSay it aloud: Data pipeline questionsFull plan: Ace the pipeline round
Python and coding
Read: Python chaptersDrill: Python practiceSay it aloud: Python questionsFull plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the Palantir loop
Run the question deckThen repeat your weakest topic once more.