Interview guides · Cushman & Wakefield

Cushman & Wakefield Software Engineer Interview Guide

Cushman & Wakefield Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at Cushman & Wakefield plays a pivotal role in bridging the gap between physical real estate and cutting-edge digital technology. As one of the world's largest commercial real estate services firms, the company relies heavily on sophisticated technology platforms to manage billions of square feet of property, optimize energy usage, and deliver data-driven insights to clients. In this role, you are not just writing code; you are building the digital foundation that drives smart building initiatives, property management workflows, and complex financial analysis tools. The impact of your work is felt globally. Software engineering teams at Cushman & Wakefield design and maintain systems that process massive amounts of IoT sensor data, integrate with complex building automation systems (including HVAC and lighting control), and streamline project management workflows.

This guide is scoped to a Software Engineer candidate at Cushman & Wakefield.

Focus areas

Interview process management
Domain knowledge: HVAC
Experience-to-role alignment

The Cushman & Wakefield interview process

Cushman & Wakefield candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening Call

A call with a recruiter to discuss your background, high-level technical experience, and role requirements.

What to demonstrate

A call with a recruiter to discuss your background, high-level technical experience, and role requirements
Depth in Interview process management

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.

Technical and Domain Focus

Involves practical coding discussions, system design reviews, or deep dives into previous work experience.

What to demonstrate

Involves practical coding discussions, system design reviews, or deep dives into previous work experience
Depth in Interview process management

How to prepare

Answer aloud and timed: How would you design a software solution to monitor and optimize energy efficiency across a portfolio of commercial properties?
Answer aloud and timed: Describe a time when you had to integrate a legacy hardware system or proprietary API with a modern web application.

Leadership Conversations

Discussions with local leadership or department heads to assess culture fit, communication style, and strategic alignment.

What to demonstrate

Discussions with local leadership or department heads to assess culture fit, communication style, and strategic alignment
Depth in Interview process management

How to prepare

Answer aloud and timed: What experience do you have working with project management software, civil engineering tools, or property management systems?
Answer aloud and timed: How would you architect a real-time data ingestion pipeline for IoT sensors tracking temperature and occupancy in a high-rise building?

Where candidates lose points

Understand the Business Model

Before your interview, research Cushman & Wakefield's core business lines. Understanding how property management, leasing, and facilities operations generate value will help you contextually frame your technical answers.

Highlight Integration Experience

Commercial real estate technology heavily relies on integrating disparate systems. Emphasize any experience you have working with third-party APIs, legacy system migrations, or hardware-software communication.

Prepare Your Motivation Story

Be ready to explain why you want to apply your engineering skills to commercial real estate. If you are interviewing in regions like Singapore where a motivational essay is requested, spend time refining your personal narrative around technology's role in physical spaces.

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?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Explain how you would structure database schemas to represent complex, hierarchical commercial real estate por

Medium · System Design & Architecture

Explain how you would structure database schemas to represent complex, hierarchical commercial real estate portfolios (e.g., regions, properties, buildings, floors, and individual suites).

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?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

How does your prior software engineering experience align with the requirements of this specific role?

Medium · Domain & Experience Alignment

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?

Can you explain your familiarity with HVAC systems, smart building technologies, or IoT integration?

Medium · Domain & Experience Alignment

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 would you design a software solution to monitor and optimize energy efficiency across a portfolio of comme

Medium · Domain & Experience Alignment

How would you design a software solution to monitor and optimize energy efficiency across a portfolio of commercial properties?

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Describe a time when you had to integrate a legacy hardware system or proprietary API with a modern web applic

Medium · Domain & Experience Alignment

Describe a time when you had to integrate a legacy hardware system or proprietary API with a modern web application.

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?

What experience do you have working with project management software, civil engineering tools, or property man

Medium · Domain & Experience Alignment

What experience do you have working with project management software, civil engineering tools, or property management systems?

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 would you architect a real-time data ingestion pipeline for IoT sensors tracking temperature and occupancy

Medium · System Design & Architecture

How would you architect a real-time data ingestion pipeline for IoT sensors tracking temperature and occupancy in a high-rise building?

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Design a system that allows regional managers and local leadership to access localized property maintenance re

Medium · System Design & Architecture

Design a system that allows regional managers and local leadership to access localized property maintenance reports securely.

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

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 Cushman & Wakefield candidates report.

Day 1 — Map the Cushman & Wakefield loop

Write out the reported sequence: Initial Screening Call, Technical and Domain Focus, Leadership Conversations.
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 3 reported rounds, with the weakest marked.

Day 2 — Work Interview process management

Spend the session on Interview process management, which Cushman & Wakefield candidates report being tested on.
Write one worked example in Interview process management and time yourself on it.

Deliverable: One timed worked example in Interview process management.

Day 3 — Work Domain knowledge: HVAC

Spend the session on Domain knowledge: HVAC, which Cushman & Wakefield candidates report being tested on.
Write one worked example in Domain knowledge: HVAC and time yourself on it.

Deliverable: One timed worked example in Domain knowledge: HVAC.

Day 4 — Work Experience-to-role alignment

Spend the session on Experience-to-role alignment, which Cushman & Wakefield candidates report being tested on.
Write one worked example in Experience-to-role alignment and time yourself on it.

Deliverable: One timed worked example in Experience-to-role alignment.

Day 5 — Answer out loud: Domain & Experience Alignment

Answer aloud, timed: How does your prior software engineering experience align with the requirements of this specific role?
Answer aloud, timed: Can you explain your familiarity with HVAC systems, smart building technologies, or IoT integration?

Deliverable: Spoken answers to 2 reported Domain & Experience Alignment question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

Answer aloud, timed: How would you architect a real-time data ingestion pipeline for IoT sensors tracking temperature and occupancy in a high-rise building?
Answer aloud, timed: Design a system that allows regional managers and local leadership to access localized property maintenance reports securely.

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral & Cultural Fit

Answer aloud, timed: Why are you interested in working as a Software Engineer at a commercial real estate firm like Cushman & Wakefield?
Answer aloud, timed: Tell me about a time you had to collaborate with non-technical stakeholders, such as regional managers or operations teams, to deliver a software feature.

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

Behavioural preparation

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

How do you handle data synchronization and consistency issues when dealing with offline-first mobile applicati

Medium · System Design & Architecture

How do you handle data synchronization and consistency issues when dealing with offline-first mobile applications used by field technicians?

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?

Why are you interested in working as a Software Engineer at a commercial real estate firm like Cushman & Wakef

Medium · Behavioral & Cultural Fit

Why are you interested in working as a Software Engineer at a commercial real estate firm like Cushman & Wakefield?

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?

Tell me about a time you had to collaborate with non-technical stakeholders, such as regional managers or oper

Medium · Behavioral & Cultural Fit

Tell me about a time you had to collaborate with non-technical stakeholders, such as regional managers or operations teams, to deliver a software feature.

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?

Describe a challenging project you worked on. What went wrong, and how did you navigate the technical and inte

Medium · Behavioral & Cultural Fit

Describe a challenging project you worked on. What went wrong, and how did you navigate the technical and interpersonal challenges?

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?

How do you prioritize tasks and manage your time when working on multiple, competing deliverables in a fast-pa

Medium · Behavioral & Cultural Fit

How do you prioritize tasks and manage your time when working on multiple, competing deliverables in a fast-paced environment?

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?