Interview guides · Coalition Technologies

Coalition Technologies Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Coalition Technologies, you are at the forefront of building the infrastructure that powers a new category of risk management and insurance. Your work directly impacts the company’s ability to assess, mitigate, and respond to cyber threats for businesses globally. By engineering robust, scalable, and secure systems, you play a critical role in protecting digital assets and ensuring the resilience of our clients. This position demands a blend of technical precision and product-minded thinking. You will contribute to core platforms, scanning engines, or internal tools that require a high degree of reliability and performance. Whether you are optimizing backend data pipelines or crafting intuitive frontend interfaces, your contributions are immediate and visible.

This guide is scoped to a Software Engineer candidate at Coalition Technologies.

Focus areas

System design
Problem solving (coding assessments)
Coding challenges (online timed)

The Coalition Technologies interview process

Coalition Technologies candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial screening to establish alignment between the candidate and the role.

What to demonstrate

Initial screening to establish alignment between the candidate and the role
Depth in System design

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 Assessments

A series of technical evaluations to assess coding skills and problem-solving abilities.

What to demonstrate

A series of technical evaluations to assess coding skills and problem-solving abilities
Depth in System design

How to prepare

Answer aloud and timed: Explain your approach to handling promises and asynchronous data in a TypeScript/JavaScript environment.
Answer aloud and timed: Solve a classic algorithmic problem, such as binary search or string manipulation, with an emphasis on code quality.

Behavioral Assessments

Evaluations focusing on communication skills and teamwork in a collaborative setting.

What to demonstrate

Evaluations focusing on communication skills and teamwork in a collaborative setting
Depth in System design

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 assessments above and write down what you would ask to confirm before it.

Stakeholder Meetings

Meetings with peer engineers, hiring managers, and possibly product or leadership team members.

What to demonstrate

Meetings with peer engineers, hiring managers, and possibly product or leadership team members
Depth in System design

How to prepare

Answer aloud and timed: How do you handle a situation where you disagree with a teammate’s technical approach?
Answer aloud and timed: Describe a project you worked on that you are particularly proud of and why.

Final Onsite Panels

Final round of interviews conducted onsite, typically involving multiple panels.

What to demonstrate

Final round of interviews conducted onsite, typically involving multiple panels
Depth in System design

How to prepare

Answer aloud and timed: How do you prioritize tasks when you have multiple competing deadlines?
Answer aloud and timed: Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder.

Where candidates lose points

Prioritize Code Quality

Even in a quick technical screen, prioritize writing clean, readable, and maintainable code over "quick and dirty" solutions.

Communicate Proactively

Your interviewer wants to see how you think. Talk through your assumptions, potential trade-offs, and why you are choosing one approach over another.

Prepare Your "Why

Have a clear, compelling story about why you want to work at Coalition Technologies specifically. Connect your personal career goals with the company's mission.

Utilize the Recruiter

Your recruiter is your best advocate. If you are unsure about the format of a round, ask for clarification or preparation tips.

Questions & practice

Explain your approach to handling promises and asynchronous data in a TypeScript/JavaScript environment.

Medium · Technical & Coding Proficiency

Approach

Say what the runtime actually does before reasoning about the code.
Name what is shared across threads and what owns each piece of state.
Identify the window where an invariant is briefly untrue.
Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

What happens if two callers reach this at the same time?
Where could this allocate more than you expect?

Solve a classic algorithmic problem, such as binary search or string manipulation, with an emphasis on code qu

Medium · Technical & Coding Proficiency

Solve a classic algorithmic problem, such as binary search or string manipulation, with an emphasis on code quality.

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?

Write a basic Python class and accompanying tests to demonstrate your grasp of OOP principles.

Medium · Technical & Coding Proficiency

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?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
How do you verify the backfill actually finished, given rows are still being inserted while it runs?

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?

Design an engine class preloaded with specific problem-solving requirements.

Medium · Technical & Coding Proficiency

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?

Fetch data from an API and render it efficiently using React components.

Medium · Technical & Coding Proficiency

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?

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Coalition Technologies candidates report.

Day 1 — Map the Coalition Technologies loop

Write out the reported sequence: Recruiter Screen, Technical Assessments, Behavioral Assessments, Stakeholder Meetings, Final Onsite Panels.
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 5 reported rounds, with the weakest marked.

Day 2 — Work System design

Spend the session on System design, which Coalition Technologies candidates report being tested on.
Write one worked example in System design and time yourself on it.

Deliverable: One timed worked example in System design.

Day 3 — Work Problem solving (coding assessments)

Spend the session on Problem solving (coding assessments), which Coalition Technologies candidates report being tested on.
Write one worked example in Problem solving (coding assessments) and time yourself on it.

Deliverable: One timed worked example in Problem solving (coding assessments).

Day 4 — Work Coding challenges (online timed)

Spend the session on Coding challenges (online timed), which Coalition Technologies candidates report being tested on.
Write one worked example in Coding challenges (online timed) and time yourself on it.

Deliverable: One timed worked example in Coding challenges (online timed).

Day 5 — Answer out loud: Technical & Coding Proficiency

Answer aloud, timed: Design an engine class preloaded with specific problem-solving requirements.
Answer aloud, timed: Fetch data from an API and render it efficiently using React components.

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

Day 6 — Answer out loud: Behavioral & Values Alignment

Answer aloud, timed: Tell me about a time you faced a difficult technical challenge and how you resolved it.
Answer aloud, timed: How do you handle a situation where you disagree with a teammate’s technical approach?

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

Day 7 — Dry run for Coalition Technologies

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.