Interview guides · Capital Technology Group

Capital Technology Group Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Capital Technology Group, you are at the intersection of robust infrastructure and high-stakes technical delivery. This role is pivotal in maintaining the integrity and scalability of our systems, whether you are focused on Public Key Infrastructure (PKI), Cloud Architecture, or Full Stack development. You will be expected to bridge the gap between complex architectural requirements and clean, maintainable code that powers our core business operations. This position offers significant influence over our technology stack and operational efficiency. You will not just be writing code; you will be solving structural problems that impact our reliability and security posture. Successful engineers here are those who thrive in environments where technical precision meets a clear, mission-driven objective.

This guide is scoped to a Software Engineer candidate at Capital Technology Group.

Focus areas

PKI (Public Key Infrastructure)
Git (Version Control)
Java

The Capital Technology Group interview process

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

Initial Screening

The process begins with a screening to assess basic qualifications and fit for the role.

What to demonstrate

The process begins with a screening to assess basic qualifications and fit for the role
Depth in PKI (Public Key Infrastructure)

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

Candidates undergo various technical assessments to evaluate their hard skills.

What to demonstrate

Candidates undergo various technical assessments to evaluate their hard skills
Depth in PKI (Public Key Infrastructure)

How to prepare

Answer aloud and timed: Explain the difference between a Git commit, merge, and tag.
Answer aloud and timed: Describe a time you had to troubleshoot a difficult production issue.

Panel-Based Evaluations

Candidates participate in panel interviews focusing on real-world scenarios and collaborative discussions.

What to demonstrate

Candidates participate in panel interviews focusing on real-world scenarios and collaborative discussions
Depth in PKI (Public Key Infrastructure)

How to prepare

Answer aloud and timed: How do you communicate technical blockers to non-technical stakeholders?
Answer aloud and timed: Look at this Java snippet: what are the potential bugs or performance bottlenecks?

Where candidates lose points

Master the Basics

Ensure your Git knowledge is practical. Know how to handle branching, merging, and tagging, as this is a core part of our daily workflow.

Be Transparent

If you encounter a question you don't know, it is better to explain how you would find the answer rather than guessing.

Review Your Basics

Spend time reviewing Java and JavaScript fundamentals, as these are the primary languages used in our technical segments.

Questions & practice

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

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.
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.
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.
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

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?

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?

What are the common pitfalls when working with SQL queries in high-volume applications?

Medium · Technical Fundamentals and Debugging

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?

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?

Publish rate-limit and deadline semantics the edge actually enforces

Easy · rate limiting · backpressure · retries

The edge API serves about 3k requests/second steady and 9k at peak against a 400 ms p99 budget, with an explicit bounded concurrency limit per instance. Limits exist per principal and per tenant. Callers are a partner integration running nightly bulk loads and a browser app. Specify the counting algorithm and window, which limit a request is charged against, the headers a well-behaved client reads, the status and body when a limit is hit, how that differs from the response when an instance is shedding load, and what each caller does with each.

Approach

Choose the counter and name its failure mode. Fixed windows admit nearly twice the limit across a boundary - a full burst at the end of one window and another at the start of the next. A token bucket states sustained rate and burst separately, which is exactly what a nightly bulk load needs. A sliding-window counter is more faithful and costs more state per key. State the choice and the burst it permits.
Charge each request against both keys and reject on the stricter. The tenant limit protects the shared primary, which absorbs roughly 1.2k writes/second in total; the per-principal limit stops one credential inside a tenant from consuming that tenant's whole allowance. The tenant is the fairness unit for the same reason it is the leading column of every index.
Advertise limit, remaining and reset for the binding key on every response, not only on rejections, so a client can pace before it is refused. Pick one naming scheme - the RateLimit-* draft fields or an X-prefixed set - document the units, and never change them afterwards.
Separate two rejections that look identical to a naive client. 429 means this caller exceeded its own share and Retry-After is a real schedule it should obey. 503 means the instance is at its concurrency bound and shedding, which is a statement about the server; a fleet-wide 503 retried on a fixed delay resynchronises every client into one stampede, so full jitter is mandatory there and the delay is the client's guess, not ours.

Follow-up

One tenant stays under its limit and still degrades everyone else during a backfill. What changes - the limiter, the worker concurrency caps, or both?
How are counters kept correct across 20 to 40 stateless instances, and what does your answer cost per request?

Shard by tenant when one tenant outgrows a single shard

Hard · sharding · hot partition · routing

One primary holds resource, resource_revision, outbox_event and idempotency_key for every tenant and is at its write ceiling at 1.2k writes/second. tenant_id leads every index. Shard across eight primaries. One tenant holds 22% of all rows and by itself exceeds a single shard's write capacity. Design the routing, the split of that tenant, and the online move of a tenant between shards with writes continuing. State what breaks for queries that are tenant-scoped today, and exactly what a write must do when it arrives at the old shard after the cutover.

Approach

Route on a unit smaller than a tenant from the start. Make the routing key (tenant_id, bucket) with a fixed bucket count - 64 over eight shards - and keep a directory mapping each (tenant_id, bucket) to a shard, carrying a version and cached in every service. An ordinary tenant has all 64 buckets pointing at one shard and behaves exactly as it does today; only the hot tenant has its buckets spread. Hashing tenant_id alone spreads tenants evenly, gives you no way to move one, and has no answer at all for a tenant larger than a node. The bucket count is the part you cannot change later without rehashing rows, so pick it well above the shard count and rebalance by moving buckets, not by re-bucketing.
For the tenant that exceeds one node, its buckets must land on different shards - that is the whole point of bucketing it, and buckets confined to its own shard would rename the rows while leaving every write on the node whose ceiling it already exceeds. Size it from measured numbers rather than from its row share: 22% of rows says nothing about write rate. The current primary tops out near 1.2k writes/second on this hardware and workload, so a tenant peaking at W writes/second needs its buckets spread over at least ceil(W / headroom-per-shard) shards, where the divisor is the share of each shard's ceiling you are willing to give it while that shard still serves other tenants - not the full 1.2k. Size on its peak, not its mean.
Fix the co-location invariant at the right grain. What must commit in one transaction is a resource, its resource_revision row and its outbox_event row, so the bucket is a property of the resource: derive it once at creation and stamp it into resource_id, and every later revision and event routes with its parent for free. Per-tenant co-location was never the requirement, and mistaking it for one is what makes a tenant look unsplittable. What genuinely breaks is an invariant spanning two resources of one tenant - a per-tenant counter, uniqueness across its resources - which now needs either a home-shard table or two-phase commit, and 2PC at this write rate is not a serious option.
Keep the idempotency constraint arbitrating, because it is now enforced per shard. PRIMARY KEY (tenant_id, idempotency_key) only continues to reject a retry if the same key always lands on the same shard, so derive the create-path bucket from hash(tenant_id, idempotency_key) and mint the new resource_id inside that bucket, which also puts the key row and the resource it guards in one transaction. A bucket chosen from anything that differs between a request and its retry - a timestamp, the worker id, a client-supplied resource id - splits one key across two shards, both inserts succeed, and the write endpoint's retry safety is silently gone.

Follow-up

The fence lasts 90 seconds instead of 4 because the final delta keeps growing. What is happening, and what do you do while the tenant is fenced?
Two tenants must merge into one account. What does that cost under this scheme, and which step is not reversible?

Describe a time you had to troubleshoot a difficult production issue.

Medium · Behavioral and Git Proficiency

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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?

Look at this Java snippet: what are the potential bugs or performance bottlenecks?

Medium · Technical Fundamentals and Debugging

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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?

How would you refactor this specific function to improve readability and efficiency?

Medium · Technical Fundamentals and Debugging

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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?

Explain the core principles of React components and state management.

Medium · Technical Fundamentals and Debugging

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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?

How do you approach debugging a system that is failing intermittently?

Medium · Technical Fundamentals and Debugging

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
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 Capital Technology Group candidates report.

Day 1 — Map the Capital Technology Group loop

Write out the reported sequence: Initial Screening, Technical Assessments, Panel-Based Evaluations.
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 PKI (Public Key Infrastructure)

Spend the session on PKI (Public Key Infrastructure), which Capital Technology Group candidates report being tested on.
Write one worked example in PKI (Public Key Infrastructure) and time yourself on it.

Deliverable: One timed worked example in PKI (Public Key Infrastructure).

Day 3 — Work Git (Version Control)

Spend the session on Git (Version Control), which Capital Technology Group candidates report being tested on.
Write one worked example in Git (Version Control) and time yourself on it.

Deliverable: One timed worked example in Git (Version Control).

Day 4 — Work Java

Spend the session on Java, which Capital Technology Group candidates report being tested on.
Write one worked example in Java and time yourself on it.

Deliverable: One timed worked example in Java.

Day 5 — Answer out loud: Behavioral and Git Proficiency

Answer aloud, timed: Tell me about your experience with Git workflows.
Answer aloud, timed: How do you handle merge conflicts in a team environment?

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

Day 6 — Answer out loud: Technical Fundamentals and Debugging

Answer aloud, timed: Look at this Java snippet: what are the potential bugs or performance bottlenecks?
Answer aloud, timed: How would you refactor this specific function to improve readability and efficiency?

Deliverable: Spoken answers to 2 reported Technical Fundamentals and Debugging question(s), under time.

Day 7 — Dry run for Capital Technology Group

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.

Tell me about your experience with Git workflows.

Medium · Behavioral and Git Proficiency

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 merge conflicts in a team environment?

Medium · Behavioral and Git Proficiency

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?

Explain the difference between a Git commit, merge, and tag.

Medium · Behavioral and Git Proficiency

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 communicate technical blockers to non-technical stakeholders?

Medium · Behavioral and Git Proficiency

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?