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Kent Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Kent, you are joining an organization at the intersection of complex engineering and digital transformation. Your work is not merely about writing code; it is about building the robust, scalable systems that power critical infrastructure, energy projects, and industrial solutions. You will contribute to high-stakes environments where reliability, precision, and performance are non-negotiable. This role is pivotal because you serve as the bridge between theoretical engineering models and practical, real-world application. Whether you are working on offshore geotechnical support systems, naval architecture calculations, or electrical engineering frameworks, your contributions directly impact the efficiency and safety of large-scale global projects.

This guide is scoped to a Software Engineer candidate at Kent.

Focus areas

  • Senior/Principal Engineering Experience
  • Geotechnical Engineering
  • Naval Architecture

The Kent interview process

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

Initial Screening

The first step involves an initial screening to assess your fit for the role.

What to demonstrate

  • The first step involves an initial screening to assess your fit for the role
  • Depth in Senior/Principal Engineering Experience

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 Screens

Prepare for technical screenings where your core technical fundamentals will be evaluated.

What to demonstrate

  • Prepare for technical screenings where your core technical fundamentals will be evaluated
  • Depth in Senior/Principal Engineering Experience

How to prepare

  • Answer aloud and timed: How do you approach debugging complex systems when traditional logs are insufficient?
  • Answer aloud and timed: What is your strategy for unit testing and validation in high-stakes engineering projects?

Behavioral Stories

In the final rounds, be ready to discuss your past projects and behavioral stories.

What to demonstrate

  • In the final rounds, be ready to discuss your past projects and behavioral stories
  • Depth in Senior/Principal Engineering Experience

How to prepare

  • Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
  • Re-read the description of the behavioral stories above and write down what you would ask to confirm before it.

Final Assessment

The process concludes with a final assessment to determine your overall fit for the team.

What to demonstrate

  • The process concludes with a final assessment to determine your overall fit for the team
  • Depth in Senior/Principal Engineering Experience

How to prepare

  • Answer aloud and timed: Describe an instance where you identified a technical bottleneck in a workflow and implemented a solution.
  • Answer aloud and timed: How do you handle ambiguity when project requirements change mid-development?

Where candidates lose points

Focus on the "Why

When discussing your past projects, explain the reasoning behind your technical choices. Understanding why a specific design pattern was chosen is more valuable than just describing the implementation.

Be Transparent About Trade-offs

Every technical decision involves trade-offs. A strong candidate acknowledges these openly and explains why they chose a specific path despite the limitations.

Prepare for Behavioral Questions

Use the STAR method (Situation, Task, Action, Result) to structure your answers. This ensures your responses remain clear and focused.

Do not overlook the importance of soft skills

The ability to collaborate with non-technical stakeholders is a significant differentiator at Kent.

Questions & practice

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

  1. Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
  2. Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
  3. Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
  4. Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

  • The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
  • The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

  1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
  2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
  3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
  4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

  • The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
  • Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

  1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
  2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
  3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
  4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

  • 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
  • A dashboard runs this for 40 owners in one page load. What changes about the design?

How do you ensure your code remains maintainable and scalable over the long term?

Medium · Technical Proficiency and Engineering Pr

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

What is your strategy for unit testing and validation in high-stakes engineering projects?

Medium · Technical Proficiency and Engineering Pr

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 balance the trade-offs between rapid prototyping and building robust production systems?

Medium · Technical Proficiency and Engineering Pr

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 how you would model a complex physical system using software.

Medium · Problem-Solving and Domain Application

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 an instance where you identified a technical bottleneck in a workflow and implemented a solution.

Medium · Problem-Solving and Domain Application

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 approach the design phase of a project before writing any code?

Medium · Problem-Solving and Domain Application

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. 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 approach debugging complex systems when traditional logs are insufficient?

Medium · Technical Proficiency and Engineering Pr

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Kent candidates report.

Day 1 — Map the Kent loop

  • Write out the reported sequence: Initial Screening, Technical Screens, Behavioral Stories, Final Assessment.
  • 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 Senior/Principal Engineering Experience

  • Spend the session on Senior/Principal Engineering Experience, which Kent candidates report being tested on.
  • Write one worked example in Senior/Principal Engineering Experience and time yourself on it.

Deliverable: One timed worked example in Senior/Principal Engineering Experience.

Day 3 — Work Geotechnical Engineering

  • Spend the session on Geotechnical Engineering, which Kent candidates report being tested on.
  • Write one worked example in Geotechnical Engineering and time yourself on it.

Deliverable: One timed worked example in Geotechnical Engineering.

Day 4 — Work Naval Architecture

  • Spend the session on Naval Architecture, which Kent candidates report being tested on.
  • Write one worked example in Naval Architecture and time yourself on it.

Deliverable: One timed worked example in Naval Architecture.

Day 5 — Answer out loud: Technical Proficiency and Engineering Principles

  • Answer aloud, timed: How do you ensure your code remains maintainable and scalable over the long term?
  • Answer aloud, timed: Describe a time you had to optimize a piece of software for a resource-constrained environment.

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

Day 6 — Answer out loud: Problem-Solving and Domain Application

  • Answer aloud, timed: Explain how you would model a complex physical system using software.
  • Answer aloud, timed: Describe an instance where you identified a technical bottleneck in a workflow and implemented a solution.

Deliverable: Spoken answers to 2 reported Problem-Solving and Domain Application question(s), under time.

Day 7 — Dry run for Kent

  • Run one full mock under time, then write down the two questions you most want to ask your interviewers.

Deliverable: A completed timed mock and two questions to ask.

Behavioural preparation

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

Describe a time you had to optimize a piece of software for a resource-constrained environment.

Medium · Technical Proficiency and Engineering Pr

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 handle ambiguity when project requirements change mid-development?

Medium · Problem-Solving and Domain Application

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 learn a new domain or technical concept quickly to complete a task.

Medium · Problem-Solving and Domain Application

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?