Argus Information & Advisory Services Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Argus Information & Advisory Services, you are stepping into a pivotal role at the intersection of big data, financial intelligence, and enterprise software. Argus, a Verisk Analytics company, manages some of the most comprehensive and complex banking and credit card datasets in the world. Your work directly enables financial institutions to benchmark performance, manage risk, and make data-driven strategic decisions. In this position, you will build and optimize the systems that ingest, process, and analyze massive volumes of highly sensitive financial data. The impact of this role is substantial; the pipelines, APIs, and applications you develop serve as the backbone for analytics platforms used by top-tier global banks. You are not just writing code; you are building the infrastructure of financial intelligence.

This guide is scoped to a Software Engineer candidate at Argus Information & Advisory Services.

Focus areas

• Technical Interviewing
• Phone Interview Skills
• Interview Process (Round Structure)

The Argus Information & Advisory Services interview process

Argus Information & Advisory Services candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Conversational Phone Screen

Initial phone call with an HR recruiter to discuss the candidate's background and fit for the role.

What to demonstrate

• Initial phone call with an HR recruiter to discuss the candidate's background and fit for the role
• Depth in Technical Interviewing

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 Phone/Video Screen

Technical interview focusing on core programming and data manipulation concepts.

What to demonstrate

• Technical interview focusing on core programming and data manipulation concepts
• Depth in Technical Interviewing

How to prepare

• Answer aloud and timed: Implement a hash map from scratch and explain how you handle collisions.
• Answer aloud and timed: Write a program to reverse a linked list.

Final Interview Loop

Virtual or on-site interviews consisting of several focused sessions testing coding abilities, system design, and behavioral alignment.

What to demonstrate

• Virtual or on-site interviews consisting of several focused sessions testing coding abilities, system design, and behavioral alignment
• Depth in Technical Interviewing

How to prepare

• Answer aloud and timed: How do you find the intersection of two large arrays efficiently?
• Answer aloud and timed: Write a SQL query to find the second highest salary from an employee table.

Where candidates lose points

Prioritize SQL Practice

Do not underestimate the database portion of the interview. At Argus Information & Advisory Services, a Software Engineer must be incredibly comfortable writing complex queries. Brush up on window functions and query optimization techniques.

Communicate Your Trade-offs

When answering system design or architecture questions, always discuss the pros and cons of your approach. Acknowledge where your design might bottleneck and how you would mitigate that risk.

Be Ready for a Fast Turnaround

Because the HR team moves quickly, ensure your schedule is flexible and that you are mentally prepared to jump into technical screens shortly after applying.

Never sacrifice code readability for cleverness during the coding interview

Argus values maintainable, clean code that other engineers on the team can easily understand and review.

Questions & practice

Write a function to detect if a string contains valid parentheses.

Medium · Coding & Algorithms

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Given a list of daily transaction amounts, write an algorithm to find the maximum contiguous subarray sum.

Medium · Coding & Algorithms

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Implement a hash map from scratch and explain how you handle collisions.

Medium · Coding & Algorithms

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write a program to reverse a linked list.

Medium · Coding & Algorithms

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

How do you find the intersection of two large arrays efficiently?

Medium · Coding & Algorithms

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write a SQL query to find the second highest salary from an employee table.

Medium · Database & SQL

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

Explain the difference between an INNER JOIN and a LEFT JOIN, and provide an example of when to use each.

Medium · Database & SQL

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

Explain what a database index is, how it works, and when it might actually degrade performance.

Medium · Database & SQL

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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 a query to calculate the cumulative sum of transactions per user over time.

Medium · Database & SQL

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. 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?

How would you design a database schema for a simple banking ledger?

Medium · Database & SQL

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?

Rebuild the search projection while it serves nine thousand queries

Medium · projections · eventual consistency · rebuild

The read-model service answers about 9k queries/second at a 120 ms p99 from a projection built off the event log, normally under 2 seconds behind. A mapping change forces a full rebuild from resource_revision, during which apply lag rises to minutes. Writes continue at 1.2k/second and events at 4k/second. Design the rebuild: how the new index is populated and cut over, how position is tracked per log partition, what the API returns alongside results so a client can tell a stale answer from a current one, and the criterion for cutting over.

Approach

1. Build into a second index and swap an alias rather than mutating the live one. The rebuild is then reversible by pointing the alias back, so a bad mapping costs a wasted rebuild instead of an outage. The price is peak storage for two full copies and double apply load during catch-up, and both numbers should be stated up front rather than discovered when disk fills.
2. Track position per log partition, not globally. Order is guaranteed only within an aggregate's partition, so progress is a vector, and the only number safe to publish is taken from the least advanced partition - the slowest one is what bounds completeness. Publishing the most advanced partition's position declares the projection current while another partition sits twenty minutes behind.
3. Make apply idempotent so the backfill and the live tail can overlap without a freeze. Each document records the aggregate_version it reflects, and any event at or below that version is discarded. This is why the event carries the full fact and not a delta: a delta cannot be discarded safely, and a consumer that calls back to read current state applies a state newer than the event it is processing, which is how a projection ends up with changes applied out of order.
4. Turn lag into a contract instead of a surprise. Return the watermark with every result set, and return the version produced by a write so the client can compare the two. A client that wrote version 7 and receives results at a watermark older than its own commit can show that its change is still landing, rather than rendering the previous value as current. Blocking the read until the projection catches up would convert a staleness problem into an availability problem at 9k queries/second, and choosing not to do that is the trade.

Follow-up

• The rebuilt index disagrees with the primary tables for 300 documents. Which is authoritative, and how do you decide without freezing writes?
• The rebuild doubles load on the log and pushes the projection p99 from 120 ms to 400 ms. What do you throttle, and which signal sets how much?

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 Argus Information & Advisory Services candidates report.

Day 1 — Map the Argus Information & Advisory Services loop

• Write out the reported sequence: Conversational Phone Screen, Technical Phone/Video Screen, Final Interview Loop.
• 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 Technical Interviewing

• Spend the session on Technical Interviewing, which Argus Information & Advisory Services candidates report being tested on.
• Write one worked example in Technical Interviewing and time yourself on it.

Deliverable: One timed worked example in Technical Interviewing.

Day 3 — Work Phone Interview Skills

• Spend the session on Phone Interview Skills, which Argus Information & Advisory Services candidates report being tested on.
• Write one worked example in Phone Interview Skills and time yourself on it.

Deliverable: One timed worked example in Phone Interview Skills.

Day 4 — Work Interview Process (Round Structure)

• Spend the session on Interview Process (Round Structure), which Argus Information & Advisory Services candidates report being tested on.
• Write one worked example in Interview Process (Round Structure) and time yourself on it.

Deliverable: One timed worked example in Interview Process (Round Structure).

Day 5 — Answer out loud: Coding & Algorithms

• Answer aloud, timed: Write a function to detect if a string contains valid parentheses.
• Answer aloud, timed: Given a list of daily transaction amounts, write an algorithm to find the maximum contiguous subarray sum.

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

Day 6 — Answer out loud: Database & SQL

• Answer aloud, timed: Write a SQL query to find the second highest salary from an employee table.
• Answer aloud, timed: Explain the difference between an INNER JOIN and a LEFT JOIN, and provide an example of when to use each.

Deliverable: Spoken answers to 2 reported Database & SQL question(s), under time.

Day 7 — Answer out loud: Behavioral & Team Fit

• Answer aloud, timed: Tell me about a time you had to optimize a piece of code or a system that was performing poorly.
• Answer aloud, timed: Describe a situation where you disagreed with a team member about a technical approach. How did you resolve it?

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

Behavioural preparation

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

Tell me about a time you had to optimize a piece of code or a system that was performing poorly.

Medium · Behavioral & Team Fit

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 disagreed with a team member about a technical approach. How did you resolve it

Medium · Behavioral & Team Fit

Describe a situation where you disagreed with a team member about a technical approach. How did you resolve it?

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?

Walk me through a complex project you recently completed. What was your specific contribution?

Medium · Behavioral & Team Fit

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 prioritize tasks when you have multiple urgent deadlines?

Medium · Behavioral & Team Fit

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 learn a new technology quickly to complete a project.

Medium · Behavioral & Team Fit

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?