Incomm Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Incomm, you are at the intersection of high-stakes payment processing and innovative financial technology. Your work directly impacts how millions of consumers and businesses interact with prepaid products, gift cards, and complex financial transaction platforms. You are not just writing code; you are building the secure, scalable backbone of a global payment ecosystem that demands precision and reliability. This role is critical to the Incomm mission of delivering seamless, secure, and innovative payment solutions. You will participate in the full software development lifecycle, from technical design and analysis of business use cases to production deployment and maintenance.

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

Focus areas

• Technical interviewing (coding + live coding)
• Data structures knowledge
• System design (whiteboarding architecture/flow)

The Incomm interview process

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

Recruiter Screen

Initial assessment of your background and interest in the position.

What to demonstrate

• Initial assessment of your background and interest in the position
• Depth in Technical interviewing (coding + live coding)

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 Deep-Dives

Series of interviews focusing on technical competency and problem-solving skills.

What to demonstrate

• Series of interviews focusing on technical competency and problem-solving skills
• Depth in Technical interviewing (coding + live coding)

How to prepare

• Answer aloud and timed: Describe your experience with Hibernate/JPA and how you optimize database queries for large datasets.
• Answer aloud and timed: What are the key considerations when securing API endpoints in a financial services environment?

Meet Hiring Managers

Interviews with hiring managers and team leads to assess fit and technical skills.

What to demonstrate

• Interviews with hiring managers and team leads to assess fit and technical skills
• Depth in Technical interviewing (coding + live coding)

How to prepare

• Prepare two projects you led end to end, each with the decision you owned and what it cost.
• Have three questions about the team's roadmap and how success is measured in the first six months.

Meet Senior Engineers

Potential discussions with senior engineers to evaluate technical knowledge and collaboration.

What to demonstrate

• Potential discussions with senior engineers to evaluate technical knowledge and collaboration
• Depth in Technical interviewing (coding + live coding)

How to prepare

• Answer aloud and timed: How would you design a system to handle high-concurrency payment transactions?
• Answer aloud and timed: Describe a time you had to migrate a system component; what were the risks and how did you mitigate them?

Where candidates lose points

Show your work

When answering technical questions, explain your reasoning process. Even if you don't know the exact answer to a niche question, showing how you would research or troubleshoot it is highly valued.

Study the stack

Ensure you are brushed up on the specific versions mentioned in the job description, such as Java 17+.

Be ready for behavioral questions

Use the STAR method (Situation, Task, Action, Result) to structure your answers to behavioral prompts.

Ask insightful questions

Use the time allotted for your questions to ask about the team's current technical challenges or their approach to production support.

Maintain professional documentation

Be ready to discuss how you document your code and system designs; this is a recurring theme in the team's expectations.

Questions & practice

How do you manage memory and performance in a high-traffic Java or.NET application?

Medium · Technical and Domain Knowledge

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Explain the role of In-memory caching (like Hazelcast) in reducing latency.

Medium · Technical and Domain Knowledge

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

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

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

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

• 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
• A dashboard runs this for 40 owners in one page load. What changes about the design?

Explain the difference between REST and SOAP and when you would choose one over the other.

Medium · Technical and Domain Knowledge

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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?

What are the key considerations when securing API endpoints in a financial services environment?

Medium · Technical and Domain Knowledge

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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?

Walk me through the architecture of a project you recently worked on; how did you handle data flow and bottlen

Medium · System Design and Architecture

Walk me through the architecture of a project you recently worked on; how did you handle data flow and bottlenecks?

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?

How would you design a system to handle high-concurrency payment transactions?

Medium · System Design and Architecture

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?

How do you ensure high availability and proper monitoring in a production environment?

Medium · System Design and Architecture

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?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

• The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
• How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Incomm candidates report.

Day 1 — Map the Incomm loop

• Write out the reported sequence: Recruiter Screen, Technical Deep-Dives, Meet Hiring Managers, Meet Senior Engineers.
• 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 Technical interviewing (coding + live coding)

• Spend the session on Technical interviewing (coding + live coding), which Incomm candidates report being tested on.
• Write one worked example in Technical interviewing (coding + live coding) and time yourself on it.

Deliverable: One timed worked example in Technical interviewing (coding + live coding).

Day 3 — Work Data structures knowledge

• Spend the session on Data structures knowledge, which Incomm candidates report being tested on.
• Write one worked example in Data structures knowledge and time yourself on it.

Deliverable: One timed worked example in Data structures knowledge.

Day 4 — Work System design (whiteboarding architecture/flow)

• Spend the session on System design (whiteboarding architecture/flow), which Incomm candidates report being tested on.
• Write one worked example in System design (whiteboarding architecture/flow) and time yourself on it.

Deliverable: One timed worked example in System design (whiteboarding architecture/flow).

Day 5 — Answer out loud: Technical and Domain Knowledge

• Answer aloud, timed: Explain the difference between REST and SOAP and when you would choose one over the other.
• Answer aloud, timed: How do you manage memory and performance in a high-traffic Java or.NET application?

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

Day 6 — Answer out loud: System Design and Architecture

• Answer aloud, timed: Walk me through the architecture of a project you recently worked on; how did you handle data flow and bottlenecks?
• Answer aloud, timed: How would you design a system to handle high-concurrency payment transactions?

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

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

• Answer aloud, timed: Tell me about a time you had to explain a complex technical issue to a non-technical stakeholder.
• Answer aloud, timed: Describe a situation where you had to adapt to changing project requirements on short notice.

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.

Describe your experience with Hibernate/JPA and how you optimize database queries for large datasets.

Medium · Technical and Domain Knowledge

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?

Describe a time you had to migrate a system component; what were the risks and how did you mitigate them?

Medium · System Design and Architecture

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?

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

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?

Describe a situation where you had to adapt to changing project requirements on short notice.

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?

How do you handle disagreements within a team regarding technical implementation?

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?

Give an example of a difficult bug you tracked down in production and your process for resolving it.

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?