Interview guides · Hilton Grand Vacations

Hilton Grand Vacations Software Engineer Interview Guide

Hilton Grand Vacations Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Hilton Grand Vacations, you are at the intersection of world-class hospitality and innovative technology. This role is essential to building and maintaining the digital platforms that power vacation ownership, booking systems, and seamless guest experiences. You will directly impact how thousands of members interact with their vacation properties, making reliability, performance, and intuitive design critical components of your daily work. Your contributions will span across customer-facing portals, internal property management systems, and complex backend integrations. The engineering culture here values practical problem-solving and an eye for user experience. Whether you are optimizing a front-end booking flow or architecting a secure backend service, your work ensures that the digital experience matches the luxury and comfort of the physical resorts.

This guide is scoped to a Software Engineer candidate at Hilton Grand Vacations.

Focus areas

Programming tests (coding/technical assessment)
Technical problem-solving under pressure
Interview structured workflow (multi-stage screening)

The Hilton Grand Vacations interview process

Hilton Grand Vacations candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial brief call to align on your background, expectations, and availability.

What to demonstrate

Initial brief call to align on your background, expectations, and availability
Depth in Programming tests (coding/technical assessment)

How to prepare

Be able to walk your CV end to end in two minutes, and say why this company specifically.
Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Phone Interview

15- to 30-minute call with the hiring manager focusing on high-level experience and portfolio.

What to demonstrate

15- to 30-minute call with the hiring manager focusing on high-level experience and portfolio
Depth in Programming tests (coding/technical assessment)

How to prepare

Answer aloud and timed: Tell me about a time you had to learn a new technology quickly to deliver a project on time.
Answer aloud and timed: How do you ensure the code you write is maintainable for future developers?

Onsite Interview

In-person or comprehensive virtual interview including a collaborative panel and one-on-one session.

What to demonstrate

In-person or comprehensive virtual interview including a collaborative panel and one-on-one session
Depth in Programming tests (coding/technical assessment)

How to prepare

Answer aloud and timed: Describe a project where you had to integrate a third-party API. What issues did you encounter?
Answer aloud and timed: Why do you want to work as a Software Engineer at Hilton Grand Vacations?

Technical Testing

Rigorous hands-on programming tests lasting up to two hours to evaluate practical coding skills.

What to demonstrate

Rigorous hands-on programming tests lasting up to two hours to evaluate practical coding skills
Depth in Programming tests (coding/technical assessment)

How to prepare

Answer aloud and timed: Tell me about a time you disagreed with a team member. How did you resolve it?
Answer aloud and timed: How do you handle receiving critical feedback on a pull request?

Where candidates lose points

Prepare for a crowd

When facing a large panel interview, it is easy to become overwhelmed. Practice addressing a room, ensure you speak clearly, and try to engage with the person who asked the question while periodically scanning the rest of the group.

Curate your portfolio

Do not just bring a list of links. Select two or three projects that best highlight your skills relevant to Hilton Grand Vacations. Be ready to discuss the architecture, the challenges, and the business impact of each.

Manage your time during coding tests

You may be given a multi-part programming test with a strict time limit (e.g., two hours). Read all the requirements before writing a single line of code, and prioritize getting a working solution before you worry about perfect optimization.

Clarify ambiguous requirements

If a technical question or coding prompt seems vague, ask clarifying questions immediately. Interviewers want to see your analytical process just as much as your final code.

Showcase your hospitality mindset

Hilton Grand Vacations is a hospitality company. Whenever possible, frame your technical decisions around how they ultimately improve the user or guest experience.

Questions & practice

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
Where does the fingerprint get computed relative to request decompression and the body-size limit?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

How would you design the database schema for a simple hotel booking system?

Medium · Technical and Programming

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

What steps do you take to secure a web application against common vulnerabilities like SQL injection or XSS?

Medium · Technical and Programming

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

Can you walk us through your portfolio and highlight a project that demonstrates your core strengths?

Medium · Portfolio and Experience

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

What was the most technically challenging aspect of the project you just showed us?

Medium · Portfolio and Experience

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

How do you ensure the code you write is maintainable for future developers?

Medium · Portfolio and Experience

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Describe a project where you had to integrate a third-party API. What issues did you encounter?

Medium · Portfolio and Experience

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

How do you optimize the load time of a web application?

Medium · Technical and Programming

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Explain the concept of dependency injection and why it is useful.

Medium · Technical and Programming

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Write a function to determine if a given string is a palindrome.

Medium · Technical and Programming

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics Hilton Grand Vacations candidates report.

Day 1 — Map the Hilton Grand Vacations loop

Write out the reported sequence: Recruiter Screen, Phone Interview, Onsite Interview, Technical Testing.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work Programming tests (coding/technical assessment)

Spend the session on Programming tests (coding/technical assessment), which Hilton Grand Vacations candidates report being tested on.
Write one worked example in Programming tests (coding/technical assessment) and time yourself on it.

Deliverable: One timed worked example in Programming tests (coding/technical assessment).

Day 3 — Work Technical problem-solving under pressure

Spend the session on Technical problem-solving under pressure, which Hilton Grand Vacations candidates report being tested on.
Write one worked example in Technical problem-solving under pressure and time yourself on it.

Deliverable: One timed worked example in Technical problem-solving under pressure.

Day 4 — Work Interview structured workflow (multi-stage screening)

Spend the session on Interview structured workflow (multi-stage screening), which Hilton Grand Vacations candidates report being tested on.
Write one worked example in Interview structured workflow (multi-stage screening) and time yourself on it.

Deliverable: One timed worked example in Interview structured workflow (multi-stage screening).

Day 5 — Answer out loud: Portfolio and Experience

Answer aloud, timed: Can you walk us through your portfolio and highlight a project that demonstrates your core strengths?
Answer aloud, timed: What was the most technically challenging aspect of the project you just showed us?

Deliverable: Spoken answers to 2 reported Portfolio and Experience question(s), under time.

Day 6 — Answer out loud: Behavioral and Team Fit

Answer aloud, timed: Why do you want to work as a Software Engineer at Hilton Grand Vacations?
Answer aloud, timed: Tell me about a time you disagreed with a team member. How did you resolve it?

Deliverable: Spoken answers to 2 reported Behavioral and Team Fit question(s), under time.

Day 7 — Answer out loud: Technical and Programming

Answer aloud, timed: How do you optimize the load time of a web application?
Answer aloud, timed: Explain the concept of dependency injection and why it is useful.

Deliverable: Spoken answers to 2 reported Technical and Programming question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Tell me about a time you had to learn a new technology quickly to deliver a project on time.

Medium · Portfolio and Experience

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?