Interview guides · Kyyba

Kyyba Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Kyyba, you occupy a critical role at the intersection of advanced technology development and strategic client delivery. Kyyba is a premier global technology solutions and professional staffing firm, meaning its engineering talent is frequently deployed to solve high-impact, complex problems for major enterprise clients. These clients span highly demanding industries, including automotive, manufacturing, healthcare, and financial services. Your work will directly influence the development of next-generation applications, embedded systems, and enterprise architectures that keep these industries moving forward. The impact of this role is substantial.

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

Focus areas

JavaScript
HTML
CSS

The Kyyba interview process

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

Telephone Screen

Detailed conversation with a Kyyba recruiter focusing on career interests, salary expectations, and a high-level review of technical background.

What to demonstrate

Detailed conversation with a Kyyba recruiter focusing on career interests, salary expectations, and a high-level review of technical background
Depth in JavaScript

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.

Internal Vetting Interview

Interview with a Kyyba technical lead or account manager to assess technical depth, cultural fit, and willingness to support the specific client opening.

What to demonstrate

Interview with a Kyyba technical lead or account manager to assess technical depth, cultural fit, and willingness to support the specific client opening
Depth in JavaScript

How to prepare

Answer aloud and timed: Explain how you manage state in a modern frontend framework versus how you handled it in older architectures.
Answer aloud and timed: What programming languages are you most comfortable using today, and how do you stay current with their latest specifications?

Client Interview

Interview with the client's reporting manager and technical team, tailored to the client's specific domain and requirements.

What to demonstrate

Interview with the client's reporting manager and technical team, tailored to the client's specific domain and requirements
Depth in JavaScript

How to prepare

Answer aloud and timed: Describe your experience with database optimization. How do you identify and resolve a slow-running query?
Answer aloud and timed: Tell me about a time when you had to deliver a critical feature under a very tight deadline with incomplete requirements.

Where candidates lose points

Highlight your adaptability

Emphasize your ability to learn new technologies quickly. Since you will be placed with external clients, showing that you can rapidly adapt to different engineering cultures is a massive selling point.

Be ready for resume-deep dives

Expect the interviewer to go through your resume line by line. Do not list any technology, library, or tool on your resume unless you can confidently discuss how you used it, why you chose it, and the problems you solved with it.

Do not exaggerate the recency of your technical experience

If a client requires immediate, senior-level contribution in a specific framework and discovers during the technical round that your experience is outdated, it will stall your candidacy across all other Kyyba client pipelines.

Questions & practice

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

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?

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?

Describe the lifecycle of an HTTP request from the moment a user presses enter in their browser to the databas

Medium · Fundamentals & System Design

Describe the lifecycle of an HTTP request from the moment a user presses enter in their browser to the database query and back.

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Walk me through the technical architecture of your most recent project and explain the design decisions you ma

Medium · Resume & Technical Foundations

Walk me through the technical architecture of your most recent project and explain the design decisions you made.

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?

When was the last time you actively wrote code in JavaScript, HTML, and CSS, and what was the scale of that ap

Medium · Resume & Technical Foundations

When was the last time you actively wrote code in JavaScript, HTML, and CSS, and what was the scale of that application?

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 how you manage state in a modern frontend framework versus how you handled it in older architectures.

Medium · Resume & Technical Foundations

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?

What programming languages are you most comfortable using today, and how do you stay current with their latest

Medium · Resume & Technical Foundations

What programming languages are you most comfortable using today, and how do you stay current with their latest specifications?

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 difference between object-oriented programming and functional programming paradigms, and when you

Medium · Fundamentals & System Design

Explain the difference between object-oriented programming and functional programming paradigms, and when you would choose one over the other.

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?

How do you design an API to ensure it is secure, versioned, and highly performant under heavy traffic?

Medium · Fundamentals & System Design

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?

What is your approach to writing unit tests, and how do you balance test coverage with rapid feature delivery?

Medium · Fundamentals & System Design

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Kyyba candidates report.

Day 1 — Map the Kyyba loop

Write out the reported sequence: Telephone Screen, Internal Vetting Interview, Client Interview.
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 JavaScript

Spend the session on JavaScript, which Kyyba candidates report being tested on.
Write one worked example in JavaScript and time yourself on it.

Deliverable: One timed worked example in JavaScript.

Day 3 — Work HTML

Spend the session on HTML, which Kyyba candidates report being tested on.
Write one worked example in HTML and time yourself on it.

Deliverable: One timed worked example in HTML.

Day 4 — Work CSS

Spend the session on CSS, which Kyyba candidates report being tested on.
Write one worked example in CSS and time yourself on it.

Deliverable: One timed worked example in CSS.

Day 5 — Answer out loud: Resume & Technical Foundations

Answer aloud, timed: Walk me through the technical architecture of your most recent project and explain the design decisions you made.
Answer aloud, timed: When was the last time you actively wrote code in JavaScript, HTML, and CSS, and what was the scale of that application?

Deliverable: Spoken answers to 2 reported Resume & Technical Foundations question(s), under time.

Day 6 — Answer out loud: Behavioral & Scenario-Based (STAR Method)

Answer aloud, timed: Tell me about a time when you had to deliver a critical feature under a very tight deadline with incomplete requirements.
Answer aloud, timed: Describe a situation where you disagreed with a senior engineer or architect. How did you present your perspective, and what was the outcome?

Deliverable: Spoken answers to 2 reported Behavioral & Scenario-Based (STAR Method) question(s), under time.

Day 7 — Answer out loud: Fundamentals & System Design

Answer aloud, timed: Explain the difference between object-oriented programming and functional programming paradigms, and when you would choose one over the other.
Answer aloud, timed: How do you design an API to ensure it is secure, versioned, and highly performant under heavy traffic?

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

Behavioural preparation

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

Describe your experience with database optimization. How do you identify and resolve a slow-running query?

Medium · Resume & Technical Foundations

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 when you had to deliver a critical feature under a very tight deadline with incomplete re

Medium · Behavioral & Scenario-Based (STAR Method

Tell me about a time when you had to deliver a critical feature under a very tight deadline with incomplete requirements.

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 situation where you disagreed with a senior engineer or architect. How did you present your perspec

Medium · Behavioral & Scenario-Based (STAR Method

Describe a situation where you disagreed with a senior engineer or architect. How did you present your perspective, and what was the outcome?

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?

Give me an example of a project that failed or missed its deadline. What did you learn, and how did you apply

Medium · Behavioral & Scenario-Based (STAR Method

Give me an example of a project that failed or missed its deadline. What did you learn, and how did you apply that knowledge to future projects?

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 handle transitioning to a new client team that uses a technology stack or development methodology y

Medium · Behavioral & Scenario-Based (STAR Method

How do you handle transitioning to a new client team that uses a technology stack or development methodology you have not worked with before?

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 explain a complex technical issue to a non-technical stakeholder or client rep

Medium · Behavioral & Scenario-Based (STAR Method

Tell me about a time you had to explain a complex technical issue to a non-technical stakeholder or client representative.

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?