Given an array of successive stock prices, write an O(n) algorithm that finds the optimal single buy index and sell index to maximize profit (Best Time to Buy and Sell Stock — LeetCode 121).

The key insight is a single left-to-right pass tracking the running minimum price and the best profit seen so far, giving O(n) time and O(1) space. Strong candidates name the greedy invariant explicitly before coding. The likely follow-up is extending to unlimited transactions (LeetCode 122) or k transactions. Clean O(n) greedy reasoning stated before the code, correct index tracking (not just the profit value), and handling of edge cases like a monotonically decreasing array.

Coding challenge: given a list of integers, return the most frequently appearing integer. Handle ties.

Build a frequency map in O(n), then scan for the maximum. Tie-breaking policy must be clarified with the interviewer first. Strong candidates discuss bucket sort as a path to O(n) if the range of values is bounded. Requirements clarification on tie-breaking, clean hash-map implementation, and willingness to discuss the O(n) bucket-sort variant unprompted.

Merge Intervals (LeetCode 56): given a collection of intervals, merge all overlapping intervals and return the non-overlapping result.

Sort by start time O(n log n), then a single linear scan greedily extending the current interval whenever the next start falls within it. Space is O(n) for output. Follow-ups commonly ask about inserting a new interval into an already-sorted list (LeetCode 57) or finding the minimum number of meeting rooms needed. Correct sort-then-sweep structure, careful handling of the merge condition (next.start <= current.end), and edge cases like a single interval or fully nested intervals.

Design a platform for integrating data from multiple government or enterprise agencies and enabling cross-entity analysis (e.g., a Palantir Foundry-style data integration layer).

Start with data ingestion: enumerate source types (RDBMS dumps, Kafka streams, flat files, classified feeds) and define a connector abstraction. Model the central ontology layer (entities + relationships). Key design decisions: schema-on-read vs schema-on-write, lineage tracking, and row-level access control (which agencies can see which records). Discuss eventual consistency for cross-agency joins. Trade-off: operational complexity of a graph store vs a columnar warehouse. Fluency with multi-source ingestion, proactive discussion of access control and data provenance as first-class requirements (not afterthoughts), and explicit trade-off articulation rather than single-answer declarations.

Build a secure audit logging system for collaborative workflows where multiple users with different permission levels act on shared datasets.

Define the audit event schema (who, what, when, on which object, from which IP/context). Write path: append-only log to an immutable store (S3/object storage) plus a real-time index for querying. Key concerns: tamper-evidence (hash chaining or WORM storage), log fan-out (one user action triggers multiple sub-actions), and query latency SLA. Discuss retention policy and GDPR redaction requirements. Immutability and tamper-evidence as first-class concerns, write-path vs read-path separation, and awareness of compliance requirements (retention, redaction) without being prompted.

Design a distributed data pipeline that processes sensor telemetry from a fleet of aircraft at scale, with requirements for fault tolerance and low latency delivery to analysts.

Ingest via a Kafka-style message bus partitioned by aircraft ID. Processing layer (Flink or Spark Streaming) handles deduplication, enrichment, and anomaly flagging. Write enriched records to a time-series store (InfluxDB / TimescaleDB) and a cold object store for replay. Key discussion: at-least-once vs exactly-once semantics, backpressure, and handling out-of-order telemetry from aircraft with intermittent connectivity. Correct streaming architecture choices, explicit discussion of delivery semantics (not just assuming exactly-once), and practical handling of real-world constraints like intermittent connectivity.

Why Palantir, and why this specific role? What do you know about what the role actually involves day-to-day?

The recruiter screen treats this as a hard filter, not a warm-up. Candidates should reference specific Palantir platforms (Foundry, Gotham, AIP) and real customer deployments (Airbus, NHS, US Army). Articulate a genuine position on working with government and defense data — interviewers are trained to detect rote answers. Connect personal engineering values to Palantir's actual work. Evidence of genuine research into Palantir's products and customers, a considered and personal stance on defense/government software, and specificity about the role rather than generic big-tech enthusiasm.

Tell me about a time you changed your mind about a technical approach or a belief you held strongly. What caused the change?

Palantir explicitly values intellectual honesty over polish. The answer should name a specific belief or design decision held, describe the concrete evidence or experience that challenged it, and state the updated view clearly. Avoid framing it as a story with a neat resolution — the thinking process is what's evaluated. Genuine intellectual honesty, specificity about what changed and why, and absence of the typical 'challenge I overcame' polish — Palantir wants evidence of real reflection.

Tell me about something you did that you are not proud of. What would you do differently?

This is the behavioral question most candidates mishandle by deflecting into a disguised strength. The interviewer wants a real failure with real stakes. Describe what went wrong, own the causal role without over-apology, state concretely what you now think differently, and show the updated behavior in a later situation if possible. Absence of the 'weakness-that-is-actually-a-strength' deflection, specific ownership of the failure, and a clearly articulated updated mental model — not just 'I learned to communicate better.'

Palantir Interview Questions

Q: Design a platform for integrating data from multiple government or enterprise agencies and enabling cross-entity analysis (e.g., a Palantir Foundry-style data integration layer).

Start with data ingestion: enumerate source types (RDBMS dumps, Kafka streams, flat files, classified feeds) and define a connector abstraction. Model the central ontology layer (entities + relationships). Key design decisions: schema-on-read vs schema-on-write, lineage tracking, and row-level access control (which agencies can see which records). Discuss eventual consistency for cross-agency joins. Trade-off: operational complexity of a graph store vs a columnar warehouse. Fluency with multi-source ingestion, proactive discussion of access control and data provenance as first-class requirements (not afterthoughts), and explicit trade-off articulation rather than single-answer declarations.

Q: Build a secure audit logging system for collaborative workflows where multiple users with different permission levels act on shared datasets.

Define the audit event schema (who, what, when, on which object, from which IP/context). Write path: append-only log to an immutable store (S3/object storage) plus a real-time index for querying. Key concerns: tamper-evidence (hash chaining or WORM storage), log fan-out (one user action triggers multiple sub-actions), and query latency SLA. Discuss retention policy and GDPR redaction requirements. Immutability and tamper-evidence as first-class concerns, write-path vs read-path separation, and awareness of compliance requirements (retention, redaction) without being prompted.

Q: Design a distributed data pipeline that processes sensor telemetry from a fleet of aircraft at scale, with requirements for fault tolerance and low latency delivery to analysts.

Ingest via a Kafka-style message bus partitioned by aircraft ID. Processing layer (Flink or Spark Streaming) handles deduplication, enrichment, and anomaly flagging. Write enriched records to a time-series store (InfluxDB / TimescaleDB) and a cold object store for replay. Key discussion: at-least-once vs exactly-once semantics, backpressure, and handling out-of-order telemetry from aircraft with intermittent connectivity. Correct streaming architecture choices, explicit discussion of delivery semantics (not just assuming exactly-once), and practical handling of real-world constraints like intermittent connectivity.

Q: Why Palantir, and why this specific role? What do you know about what the role actually involves day-to-day?

The recruiter screen treats this as a hard filter, not a warm-up. Candidates should reference specific Palantir platforms (Foundry, Gotham, AIP) and real customer deployments (Airbus, NHS, US Army). Articulate a genuine position on working with government and defense data — interviewers are trained to detect rote answers. Connect personal engineering values to Palantir's actual work. Evidence of genuine research into Palantir's products and customers, a considered and personal stance on defense/government software, and specificity about the role rather than generic big-tech enthusiasm.

Q: Tell me about a time you changed your mind about a technical approach or a belief you held strongly. What caused the change?

Palantir explicitly values intellectual honesty over polish. The answer should name a specific belief or design decision held, describe the concrete evidence or experience that challenged it, and state the updated view clearly. Avoid framing it as a story with a neat resolution — the thinking process is what's evaluated. Genuine intellectual honesty, specificity about what changed and why, and absence of the typical 'challenge I overcame' polish — Palantir wants evidence of real reflection.

Q: Tell me about something you did that you are not proud of. What would you do differently?

This is the behavioral question most candidates mishandle by deflecting into a disguised strength. The interviewer wants a real failure with real stakes. Describe what went wrong, own the causal role without over-apology, state concretely what you now think differently, and show the updated behavior in a later situation if possible. Absence of the 'weakness-that-is-actually-a-strength' deflection, specific ownership of the failure, and a clearly articulated updated mental model — not just 'I learned to communicate better.'

Palantir runs a 3-4 week loop: a 30-minute recruiter screen (mission alignment is a hard filter), a 60-90 minute HackerRank online assessment (one coding problem, one SQL query, sometimes an API integration task), a 3-4 round virtual onsite drawn from Coding, Problem Decomposition, Re-engineering, Learning, and System Design, and a closing Hiring Manager call. Every onsite round embeds roughly 20 minutes of behavioral questions — there is no standalone behavioral round. The process is notably different from other top companies: problems are implementation-heavy and user-context-framed, the Decomposition round is nearly universal, and mission-fit is treated as a hard disqualifier rather than a soft signal.

17 real questions across 3 rounds

Reported via Glassdoor Palantir SWE interview questions (QTN_2469199 confirms Merge Intervals); LeetCode company tag (github.com/krishnadey30/LeetCode-Questions-CompanyWise palantir-technologies_2year.csv) confirms LC 121; linkjob.ai Palantir HackerRank 2026 guide + assignmenthelp.net/brainly confirm the TV-shows paginated REST API OA task; Medium first-hand account by Anqi Silvia; interviewing.io Palantir interview guide; prepfully.com Palantir SWE guide 2026 · Palantir Decomposition and Re-engineering rounds are internally distinct interview types within the technical loop. Sources: Medium first-hand account by Anqi Silvia (decomposition mistakes post); Blind thread 'Palantir Learning & Decomposition Interview' (OkrwJ5Jc); interviewing.io Palantir guide; prepfully.com Palantir SWE guide 2026; linkjob.ai decomposition guide 2026; coditioning.com Palantir decomposition guide; algo.monster Palantir guide; techprep.app Palantir process 2025 — parking garage, chess, and disease-spread are confirmed example prompts across multiple independent sources

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