ITAC Solutions Software Engineer Interview Guide

The 60-second brief

As a Software Engineer working through or placed by ITAC Solutions, you play a vital role in connecting top-tier technical talent with high-impact engineering projects. Because ITAC Solutions partners with a diverse portfolio of clients ranging from cloud infrastructure providers to specialized software development firms, your engineering contributions directly shape critical products, secure network architectures, and scalable cloud solutions. Your daily work influences how organizations build, deploy, and scale modern applications, making your technical fluency and problem-solving capabilities essential to business success. The scope of engineering roles associated with ITAC Solutions spans across full-stack development, cloud engineering, network systems, and specialized API integrations.

This guide is scoped to a Software Engineer candidate at ITAC Solutions.

Focus areas

• Cloud Computing
• API Engineering
• Network Engineering

The ITAC Solutions interview process

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

Initial Technical Screening

Conducted by a senior recruiter or engineering manager to discuss your professional background and core technologies.

What to demonstrate

• Conducted by a senior recruiter or engineering manager to discuss your professional background and core technologies
• Depth in Cloud Computing

How to prepare

• Answer aloud and timed: This category evaluates your alignment with role requirements, location constraints, and operational logistics.
• Answer aloud and timed: Bullet list of realistic example questions: Would you be willing to relocate? (Keep in mind, this is often clarified early for remote positions.) What is your expected timeline for starting a new contract or full-time placement? How do you manage your schedule when working across distributed technical teams? What are your primary compensation expectations for this level of engineering? Are you comfortable working within hybrid or on-site client environments as required?

Deep-Dive Technical Assessments

Includes live coding, system design exercises, or scenario-based troubleshooting based on your engineering track.

What to demonstrate

• Includes live coding, system design exercises, or scenario-based troubleshooting based on your engineering track
• Depth in Cloud Computing

How to prepare

• Answer aloud and timed: This category tests your core engineering stack knowledge, system understanding, and hands-on capability.
• Answer aloud and timed: Bullet list of realistic example questions: How would you approach building and securing an API cloud integration? Can you explain your experience with full-stack development frameworks like React? What strategies do you use for optimizing cloud infrastructure and managing deployment costs? How do you handle troubleshooting and diagnostics in complex network or systems environments? Describe a time you implemented an automated solution or CI/CD pipeline.

Panel Interviews

Focus on behavioral competencies, cross-functional collaboration, and leadership.

What to demonstrate

• Focus on behavioral competencies, cross-functional collaboration, and leadership
• Depth in Cloud Computing

How to prepare

• Answer aloud and timed: This category assesses how you collaborate, communicate, and navigate technical roadblocks under pressure.
• Answer aloud and timed: Bullet list of realistic example questions: Tell me about a time you had to resolve a critical production bug under a tight deadline. How do you prioritize tasks when managing multiple competing engineering requirements? Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder. How do you handle feedback or code reviews from senior team members? Give an example of how you approached a project requirement that changed mid-development.

Where candidates lose points

Clarify logistics early

Always verify location, hybrid expectations, and remote work policies during your very first conversation with the talent representative to prevent misalignments.

Focus on practical impact

When discussing past projects, emphasize the business value, scalability improvements, and performance metrics you achieved rather than just listing technologies used.

Prepare structured behavioral stories

Use the STAR method to frame your past experiences, highlighting how you handled technical disagreements, tight deadlines, or ambiguous requirements.

Ask insightful technical questions

Show your engagement by asking deep questions about the team's deployment pipelines, code review processes, and architectural bottlenecks.

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

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

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

• A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
• Where does the fingerprint get computed relative to request decompression and the body-size limit?

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach

1. Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
2. Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
3. Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
4. Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.

Follow-up

• The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
• The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

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

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

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

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

This category evaluates your alignment with role requirements, location constraints, and operational logistics

Medium · Logistical and Screening Questions

This category evaluates your alignment with role requirements, location constraints, and operational logistics.

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?

Bullet list of realistic example questions: Would you be willing to relocate? (Keep in mind, this is often cla

Medium · Logistical and Screening Questions

Bullet list of realistic example questions: Would you be willing to relocate? (Keep in mind, this is often clarified early for remote positions.) What is your expected timeline for starting a new contract or full-time placement? How do you manage your schedule when working across distributed technical teams? What are your primary compensation expectations for this level of engineering? Are you comfortable working within hybrid or on-site client environments as required?

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?

This category tests your core engineering stack knowledge, system understanding, and hands-on capability.

Medium · Technical and Domain Expertise

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?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

1. Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
2. Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
3. Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
4. Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

• The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
• You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics ITAC Solutions candidates report.

Day 1 — Map the ITAC Solutions loop

• Write out the reported sequence: Initial Technical Screening, Deep-Dive Technical Assessments, Panel Interviews.
• 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 Cloud Computing

• Spend the session on Cloud Computing, which ITAC Solutions candidates report being tested on.
• Write one worked example in Cloud Computing and time yourself on it.

Deliverable: One timed worked example in Cloud Computing.

Day 3 — Work API Engineering

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

Deliverable: One timed worked example in API Engineering.

Day 4 — Work Network Engineering

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

Deliverable: One timed worked example in Network Engineering.

Day 5 — Answer out loud: Logistical and Screening Questions

• Answer aloud, timed: This category evaluates your alignment with role requirements, location constraints, and operational logistics.
• Answer aloud, timed: Bullet list of realistic example questions: Would you be willing to relocate? (Keep in mind, this is often clarified early for remote positions.) What is your expected timeline for starting a new contract or full-time placement? How do you manage your schedule when working across distributed technical teams? What are your primary compensation expectations for this level of engineering? Are you comfortable working within hybrid or on-site client environments as required?

Deliverable: Spoken answers to 2 reported Logistical and Screening Questions question(s), under time.

Day 6 — Answer out loud: Technical and Domain Expertise

• Answer aloud, timed: This category tests your core engineering stack knowledge, system understanding, and hands-on capability.
• Answer aloud, timed: Bullet list of realistic example questions: How would you approach building and securing an API cloud integration? Can you explain your experience with full-stack development frameworks like React? What strategies do you use for optimizing cloud infrastructure and managing deployment costs? How do you handle troubleshooting and diagnostics in complex network or systems environments? Describe a time you implemented an automated solution or CI/CD pipeline.

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

Day 7 — Answer out loud: Behavioral and Problem-Solving

• Answer aloud, timed: This category assesses how you collaborate, communicate, and navigate technical roadblocks under pressure.
• Answer aloud, timed: Bullet list of realistic example questions: Tell me about a time you had to resolve a critical production bug under a tight deadline. How do you prioritize tasks when managing multiple competing engineering requirements? Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder. How do you handle feedback or code reviews from senior team members? Give an example of how you approached a project requirement that changed mid-development.

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

Behavioural preparation

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

Bullet list of realistic example questions: How would you approach building and securing an API cloud integrat

Medium · Technical and Domain Expertise

Bullet list of realistic example questions: How would you approach building and securing an API cloud integration? Can you explain your experience with full-stack development frameworks like React? What strategies do you use for optimizing cloud infrastructure and managing deployment costs? How do you handle troubleshooting and diagnostics in complex network or systems environments? Describe a time you implemented an automated solution or CI/CD pipeline.

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?

This category assesses how you collaborate, communicate, and navigate technical roadblocks under pressure.

Medium · Behavioral and Problem-Solving

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?

Bullet list of realistic example questions: Tell me about a time you had to resolve a critical production bug

Medium · Behavioral and Problem-Solving

Bullet list of realistic example questions: Tell me about a time you had to resolve a critical production bug under a tight deadline. How do you prioritize tasks when managing multiple competing engineering requirements? Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder. How do you handle feedback or code reviews from senior team members? Give an example of how you approached a project requirement that changed mid-development.

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?