Interview guides · FinQuery

FinQuery Software Engineer Interview Guide

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

The 60-second brief

FinQuery hires Software Engineers; this guide collects what candidates report about the process.

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

Focus areas

C#
.NET Core
Full-stack development

The FinQuery interview process

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

Recruiter Screen

Initial screening by a recruiter to assess candidate fit for the role.

What to demonstrate

Initial screening by a recruiter to assess candidate fit for the role
Depth in C#

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 Discussions

Deep-dive technical discussions to evaluate the candidate's technical depth and software architecture approach.

What to demonstrate

Deep-dive technical discussions to evaluate the candidate's technical depth and software architecture approach
Depth in C#

How to prepare

Answer aloud and timed: What is your experience with React or similar modern front-end frameworks when integrating with complex data models?
Answer aloud and timed: How do you approach optimizing database queries in PostgreSQL or SQL Server?

Coding Exercises

Hands-on coding exercises to demonstrate practical coding skills.

What to demonstrate

Hands-on coding exercises to demonstrate practical coding skills
Depth in C#

How to prepare

Answer aloud and timed: What is your familiarity with microservices architecture and how do you handle service-to-service communication?
Answer aloud and timed: Describe a situation where you had to debug a difficult production issue. What was your process?

Leadership Discussions

Final discussions with executive leadership to assess strategic vision and cultural fit.

What to demonstrate

Final discussions with executive leadership to assess strategic vision and cultural fit
Depth in C#

How to prepare

Answer aloud and timed: How do you define "clean code," and how do you enforce those standards during peer reviews?
Answer aloud and timed: If you were given a legacy piece of code with "code smells," how would you prioritize the refactoring effort?

Where candidates lose points

Own your narrative

When discussing past projects, be ready to explain the "why" behind your technical decisions, not just the "what."

Focus on the business

Show that you understand the financial domain. Connecting your technical solution to a business outcome (e.g., reducing risk or boosting efficiency) is highly valued.

Prepare to collaborate

Since you will be working with various teams, demonstrate your ability to listen and incorporate feedback during technical discussions.

Embrace the stack

Be comfortable speaking about the nuances of the.NET and React ecosystem, as these are core to the daily work.

Questions & practice

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

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

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.
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.
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.
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?

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?

How do you approach optimizing database queries in PostgreSQL or SQL Server?

Medium · Technical & Domain Proficiency

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?

Stop tag and share joins from fanning out a page

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

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

Approach

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

Follow-up

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

How have you utilized C# and.NET Core to build scalable backend services in your previous projects?

Medium · Technical & Domain 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?

Can you walk me through a complex architectural challenge you faced while building a REST API?

Medium · Technical & Domain 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?

What is your familiarity with microservices architecture and how do you handle service-to-service communicatio

Medium · Technical & Domain Proficiency

What is your familiarity with microservices architecture and how do you handle service-to-service communication?

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?

How do you define "clean code," and how do you enforce those standards during peer reviews?

Medium · Problem-Solving & Engineering Judgment

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

If you were given a legacy piece of code with "code smells," how would you prioritize the refactoring effort?

Medium · Problem-Solving & Engineering Judgment

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

How do you incorporate AI-assisted development tools into your workflow to improve productivity without sacrif

Medium · Problem-Solving & Engineering Judgment

How do you incorporate AI-assisted development tools into your workflow to improve productivity without sacrificing quality?

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

Describe a situation where you had to debug a difficult production issue. What was your process?

Medium · Problem-Solving & Engineering Judgment

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 FinQuery candidates report.

Day 1 — Map the FinQuery loop

Write out the reported sequence: Recruiter Screen, Technical Discussions, Coding Exercises, Leadership Discussions.
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 C#

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

Deliverable: One timed worked example in C#.

Day 3 — Work .NET Core

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

Deliverable: One timed worked example in .NET Core.

Day 4 — Work Full-stack development

Spend the session on Full-stack development, which FinQuery candidates report being tested on.
Write one worked example in Full-stack development and time yourself on it.

Deliverable: One timed worked example in Full-stack development.

Day 5 — Answer out loud: Technical & Domain Proficiency

Answer aloud, timed: How have you utilized C# and.NET Core to build scalable backend services in your previous projects?
Answer aloud, timed: Can you walk me through a complex architectural challenge you faced while building a REST API?

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

Day 6 — Answer out loud: Problem-Solving & Engineering Judgment

Answer aloud, timed: Describe a situation where you had to debug a difficult production issue. What was your process?
Answer aloud, timed: How do you define "clean code," and how do you enforce those standards during peer reviews?

Deliverable: Spoken answers to 2 reported Problem-Solving & Engineering Judgment question(s), under time.

Day 7 — Answer out loud: Behavioral & Cultural Fit

Answer aloud, timed: Why are you interested in the intersection of software engineering and accounting automation?
Answer aloud, timed: How do you handle feedback from teammates or stakeholders when there is a disagreement on technical direction?

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

Behavioural preparation

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

What is your experience with React or similar modern front-end frameworks when integrating with complex data m

Medium · Technical & Domain Proficiency

What is your experience with React or similar modern front-end frameworks when integrating with complex data models?

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?

Describe a time you had to weigh technical debt against the need for rapid feature delivery.

Medium · Problem-Solving & Engineering Judgment

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?