Interview guides · HiQ

HiQ Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at HiQ is a pivotal role that sits at the intersection of technical innovation and client-focused problem solving. As a technology consulting firm, HiQ relies on its engineers to design, build, and deploy robust solutions that address complex business challenges for a diverse set of clients. You are not just writing code; you are crafting scalable systems and digital experiences that drive value across various industries. Working at HiQ means you will likely operate within a collaborative team environment, often transitioning between different project scopes and technologies. The role is designed for engineers who thrive on variety, possess a strong sense of ownership, and enjoy the challenge of translating abstract client requirements into high-quality, maintainable software.

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

Focus areas

Programming tests / coding assessments
Take-home coding quizzes
Python

The HiQ interview process

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

Recruiter Screen

Initial contact with a recruiter or consultant manager to discuss your background and fit for the role.

What to demonstrate

Initial contact with a recruiter or consultant manager to discuss your background and fit for the role
Depth in Programming tests / coding assessments

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.

Take-Home Coding Assignment

Complete a coding assignment that serves as a baseline for technical discussions in later rounds.

What to demonstrate

Complete a coding assignment that serves as a baseline for technical discussions in later rounds
Depth in Programming tests / coding assessments

How to prepare

Answer aloud and timed: Why do you want to work at HiQ?
Answer aloud and timed: Tell me about yourself and your professional background.

Technical Evaluation

Participate in a technical evaluation to assess your coding skills and problem-solving abilities.

What to demonstrate

Participate in a technical evaluation to assess your coding skills and problem-solving abilities
Depth in Programming tests / coding assessments

How to prepare

Answer aloud and timed: How would your coworkers describe you?
Answer aloud and timed: Can you walk me through your previous work experience and major projects?

Team-Focused Interview

Engage in a team-focused interview to evaluate your personal compatibility and fit for the consulting environment.

What to demonstrate

Engage in a team-focused interview to evaluate your personal compatibility and fit for the consulting environment
Depth in Programming tests / coding assessments

How to prepare

Answer aloud and timed: Regarding your coding test: what could be improved in your solution, or did you encounter any bugs?
Answer aloud and timed: Do you enjoy working with legacy code?

Where candidates lose points

Show your personality

HiQ interviewers want to know the person behind the code. Don't be afraid to mention hobbies or interests when asked.

Be honest about limitations

If you don't know an answer, explain how you would go about finding the solution. This shows a problem-solving mindset.

Prepare your questions

Always have 2–3 thoughtful questions for your interviewers about their experiences at the company.

Review your CV

Be prepared to talk about every project listed on your resume in detail.

Questions & practice

Can you walk me through your previous work experience and major projects?

Medium · Technical Proficiency and Problem Solvin

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Regarding your coding test: what could be improved in your solution, or did you encounter any bugs?

Medium · Technical Proficiency and Problem Solvin

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Do you enjoy working with legacy code?

Medium · Technical Proficiency and Problem Solvin

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Explain the logic behind the solution you provided in the coding test.

Medium · Technical Proficiency and Problem Solvin

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Given a specific scenario (e.g., placing items on a grid), how would you design an efficient function to satis

Medium · Technical Proficiency and Problem Solvin

Given a specific scenario (e.g., placing items on a grid), how would you design an efficient function to satisfy the requirements?

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?

Evolve the resource contract without breaking integrations you cannot upgrade

Hard · versioning · backward compatibility · deprecation

GET /v1/resources/{id} returns status from a four-value enum ('draft','active','archived','deleted'), a numeric version, and the body inline. Consumers are a browser app you deploy and roughly 300 server-side integrations, some untouched for two years, that switch exhaustively on status and parse ids as JSON numbers. You must add a 'pending_review' status, move bodies over 256 KB to a body_ref pointer, and expose per-field change history from resource_revision. Specify the compatibility policy, the wire changes, how both generations are served, and the evidence that lets you remove the old shape.

Approach

Write the policy first and date it: fields are added, never retyped or repurposed; consumers ignore unknown fields; an unknown enum value maps to a documented fallback; nothing is removed until telemetry shows no caller reads it. Then say the uncomfortable part out loud - v1 shipped without the unknown-value rule, so 300 running integrations have no fallback, and no server change can install one into code that is already deployed.
That single fact forces per-request negotiation rather than a server-side default. Keep one internal model and select a serialiser from an explicit version in the request, and default a caller that sends nothing to the oldest supported version. Defaulting to the newest is the change that breaks every integration that never asked for anything, on the day you ship.
Downgrade 'pending_review' for old callers to the nearest state they already handle, 'draft', and state the loss explicitly: those integrations cannot see review state and will treat the resource as editable. If that is unacceptable for one integration, the remedy is moving it to the new version, not a cleverer projection - there is no mapping that invents a state the client has no code for.
Make the body change additive. Old callers keep body inline; the new shape adds body_ref and a size field, and resources over the limit are served to old callers by resolving the pointer server-side or by refusing with a documented code, chosen once and published. Never repurpose body to carry the pointer: a client that renders it shows a storage key to a user, and that failure is silent, where a missing field would have been loud. While you are here, serialise BIGINT ids as strings in the new shape - a browser parsing JSON numbers gets IEEE-754 doubles, exact only to 2^53 - and treat that as its own breaking change requiring the same negotiation, not a quiet fix.

Follow-up

An old integration submits a status transition while the resource is really in 'pending_review'. What does the write path accept, and what does it reject?
Two years on you want to delete the v1 serialiser. What evidence makes that safe, and who must be contacted before it happens?

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 HiQ candidates report.

Day 1 — Map the HiQ loop

Write out the reported sequence: Recruiter Screen, Take-Home Coding Assignment, Technical Evaluation, Team-Focused 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 4 reported rounds, with the weakest marked.

Day 2 — Work Programming tests / coding assessments

Spend the session on Programming tests / coding assessments, which HiQ candidates report being tested on.
Write one worked example in Programming tests / coding assessments and time yourself on it.

Deliverable: One timed worked example in Programming tests / coding assessments.

Day 3 — Work Take-home coding quizzes

Spend the session on Take-home coding quizzes, which HiQ candidates report being tested on.
Write one worked example in Take-home coding quizzes and time yourself on it.

Deliverable: One timed worked example in Take-home coding quizzes.

Day 4 — Work Python

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

Deliverable: One timed worked example in Python.

Day 5 — Answer out loud: Behavioral and Personal Motivation

Answer aloud, timed: What are your primary motivational factors for working in software engineering?
Answer aloud, timed: Where do you see yourself in 5 or 10 years?

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

Day 6 — Answer out loud: Technical Proficiency and Problem Solving

Answer aloud, timed: Can you walk me through your previous work experience and major projects?
Answer aloud, timed: Regarding your coding test: what could be improved in your solution, or did you encounter any bugs?

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

Day 7 — Dry run for HiQ

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.