Interview guides · Argo Data

Argo Data Software Engineer Interview Guide

Argo Data Software Engineer interview guide: phone screen, multiple-choice skills test and panel rounds, with debugging and STAR prep and a 7-day plan.

The 60-second brief

Candidates describe Argo Data as a company that supplies software to banks and other financial institutions. The stack they list pairs standard web and database technologies (HTML, CSS, JavaScript, Java, SQL, XML) with proprietary programming languages used inside the company. As candidates describe the Software Engineer job, it covers maintaining and extending existing code, fixing production defects, watching system health such as CPU spikes, and taking part in code reviews, team meetings and deployment planning.

This guide is for candidates applying to Argo Data as a Software Engineer; candidates report openings from entry level up to Sr. Software Engineer. It covers the three reported rounds, the fundamentals and production-scenario questions candidates remember, the behavioral prompts, and a seven-day plan. The stages come from candidate reports, not from a published process.

Candidates report three rounds over roughly 3-5 weeks: a Phone Screen, a Skills Test and a Panel Interview.

Focus areas

Behavioral Interviewing
React
Recursion

Overview

The reported technical questions are fundamentals, not puzzle-style algorithm problems: define recursion, explain how the number 3 differs from the text "3" in JavaScript, say what XML is for, write the HTML for an unordered list, and write a function that finds the smallest value in an array. A short, correct answer with one example beats a long tour of the topic. The skills test is reported as multiple choice across general programming, HTML, SQL, Java and XML, so breadth and careful reading of code snippets matter more than depth in any one language.

The second reported group is production work: resolving a production defect, debugging when the cause is unknown, page load optimization and caching, a sudden CPU spike, and how you test and deploy so the system stays stable. These are answered as an ordered sequence (reproduce, narrow, fix, verify, prevent) tied to something you have actually shipped or supported. Because candidates also report proprietary languages in the stack, a story about learning an unfamiliar codebase or tool quickly is worth having ready.

Behavioral questions are reported as a major part of the loop, especially the panel. Six reported prompts cover a major issue you resolved, a deadline you met with a team, a disagreement with a manager or lead, a resume project you are proud of, and why you want to work at Argo Data and whether you would relocate. Candidates report many roles being based in Richardson and Dallas, Texas, so settle your relocation answer, and your work-authorization answer, before the first call.

The Argo Data interview process

Candidates describe three stages, in this order. The Skills Test is the unusual one: it is reported as a multiple-choice test rather than live coding.

Phone Screen

Candidates report a conversation with a recruiter about your background, your qualifications and location, plus a few basic technical questions. Treat it as a spoken version of the fundamentals list: short definitions with one example each, and a clear answer on where you can work. Keep the background summary tied to the stack the role lists (web technologies, Java, SQL) and to any production support you have done.

What to demonstrate

How well your background and qualifications match the role as you describe them
Whether you can give short, accurate definitions of basic programming concepts out loud
Whether your location and relocation situation fits the roles on offer

How to prepare

Write a short walk through your CV that ends on why a software role for financial institutions fits you, and say it aloud until it is smooth.
Rehearse one-sentence definitions with a tiny example for recursion, JavaScript type coercion and XML.
Decide your answer on relocating to Richardson or Dallas, Texas, and on work authorization, and state each in one clear sentence.

Skills Test

Candidates report a written or digital multiple-choice test on general programming plus HTML, SQL, Java and XML. They say it rewards attention to detail: some items hide a small syntax trick or ask for the exact output of a short snippet. Prepare by tracing code by hand rather than skimming it, and by reviewing breadth across the listed languages instead of going deep in one.

What to demonstrate

Breadth across general programming, HTML, SQL, Java and XML
Reading a short snippet and predicting its exact output
Care with syntax details that change a result

How to prepare

Build a JavaScript coercion table: 3 + "3" gives "33", "3" - 1 gives 2, 3 == "3" is true, 3 === "3" is false, typeof 3 is "number". Predict each result before running it.
Trace ten short Java snippets by hand, covering integer division, == versus equals on strings, loop bounds and ++ ordering, then run them to check.
Review SQL SELECT with JOIN, GROUP BY with HAVING, NULL comparisons, plus XML well-formedness rules and the markup for HTML lists and forms.

Panel Interview

Candidates report a series of panel interviews with senior engineers and management, often two interviewers to one candidate or a small group, mixing technical scenarios with "tell me about a time..." questions. The technical side is the production material: defects, debugging, caching, CPU spikes, deployments. Prepare numbered step sequences for the scenarios and a set of STAR stories, and practise speaking to every person at the table.

What to demonstrate

Whether you can break down a production failure into a logical, ordered resolution
Specific past examples of teamwork, obstacles and disagreement
How clearly you explain technical ideas to more than one listener

How to prepare

Write a numbered answer for the production defect, the unknown-root-cause bug, the CPU spike and the caching and page load questions, each ending in how you verify the fix.
Prepare four or five STAR stories and map each to the reported prompts: major issue, tight deadline, disagreement with a lead, proud project.
Run a mock with two listeners and practise sharing eye contact between them as you answer, including the why-Argo-Data and relocation question.

Where candidates lose points

Skimming a Java or JavaScript snippet on the skills test and picking the answer that looks familiar

Trace it line by line and write the variable values down. Practise the usual traps: 7 / 2 is 3 in Java integer division, == on strings compares references, and "3" + 1 is "31" while "3" - 1 is 2.

Answering the production defect question with a conclusion ("I would find the bug and fix it") instead of a sequence

Give ordered steps: confirm and reproduce, check what changed recently, read logs, narrow the cause, make the smallest safe fix, add a regression test, deploy carefully, verify in production, and record what you learned.

Reacting to a CPU spike by guessing at code

Start by identifying which host and which process is hot, whether load or a deployment changed at the same time, and whether it is a loop, a slow query or garbage collection. Mitigate first by rolling back or restarting if needed, then find the cause with a profile or thread dump.

Telling behavioral stories in "we" with no visible personal contribution or result

Open with the situation in two sentences, then spend the time on what you decided and did. Close with the outcome, what it cost, and what you changed afterwards. The resume-project prompt asks directly for your specific contribution.

Giving a vague answer to why Argo Data and whether you would relocate

Tie the why to facts you can state (software for banks and financial institutions, standard web and database technologies alongside proprietary languages, production support work) and to your own experience. Answer relocation plainly, with any real conditions stated.

Questions & practice

What is recursion? Can you define it and provide a simple example?

Medium · Basic Technical Trivia

Approach

Define it in one line: a function that solves a problem by calling itself on a smaller instance of the same problem. It needs a base case that returns without recursing and a recursive case that moves toward that base case.
Give a short example and trace it: in Java, int factorial(int n) { if (n <= 1) return 1; return n * factorial(n - 1); }. Then walk factorial(3) -> 3 * factorial(2) -> 3 * 2 * factorial(1) -> 6.
Explain the call stack. Each call pushes a frame, so depth n costs O(n) stack space. A missing or unreachable base case throws StackOverflowError in Java and RangeError (Maximum call stack size exceeded) in JavaScript.
Point out the limits: int overflows after 12! (13! is larger than 2,147,483,647), so use long up to 20! or BigInteger past that. Java does not eliminate tail calls, so very deep recursion can still overflow even when written tail-recursively.
Say when recursion fits naturally: trees and nested structures (directory walks, nested XML or JSON, DOM traversal). For a simple linear loop like summing an array, iteration is cheaper.

Follow-up

What happens if you remove the base case, and what error do you get in Java versus JavaScript?
Rewrite the example iteratively. Which version would you ship, and why?
Why is naive recursive Fibonacci exponential, and how does memoization bring it to O(n)?

Write a short algorithm or function to find the smallest value in an array.

Medium · Basic Technical Trivia

Approach

Use a single linear scan. Initialise min to the first element, loop from index 1, and replace min whenever a smaller value appears. This is O(n) time and O(1) extra space. An unsorted array cannot be done faster, because every element has to be looked at.
Write it in Java: if (a == null || a.length == 0) throw new IllegalArgumentException(); int min = a[0]; for (int i = 1; i < a.length; i++) if (a[i] < min) min = a[i]; return min;
Call out the edge cases: an empty or null array, a single element, all-negative values (which is why initialising to 0 is a bug), and duplicates. Initialising to Integer.MAX_VALUE works, but it silently returns MAX_VALUE for an empty array.
Know the library versions and their traps. Arrays.stream(a).min() returns an OptionalInt. In JavaScript, Math.min(...arr) returns Infinity for an empty array and can throw RangeError on very large arrays, and arr.reduce((m, x) => x < m ? x : m) throws TypeError on an empty array when no initial value is given.
Reject sorting as the method: it costs O(n log n) to answer an O(n) question. If the array is already sorted, a[0] is O(1). For a rotated sorted array with distinct values, binary search finds the minimum in O(log n).

Follow-up

Return both the minimum and the maximum in one pass. Can you use fewer than 2n comparisons by processing elements in pairs?
How would you find the second-smallest value in a single pass?
Find the minimum of a sorted array that has been rotated, in better than linear time.

What is your specific approach to resolving a production defect?

Medium · Problem Solving & Production Scenarios

Approach

Triage first. Confirm the defect is real, then scope it: which users, environment, version and feature are affected, since when, and how severe it is. Collect evidence before anything changes: error messages, stack traces, request or transaction IDs, timestamps and logs.
Mitigate before fixing. If the defect lines up with a recent release, roll back or turn off the feature flag. Tell stakeholders the impact and the next update time. For a system that writes records, check whether bad data was persisted and needs correcting.
Find the root cause. Reproduce it in a lower environment with matching data and configuration, read the release diff, and narrow it down through logs. Capture the bug as a failing automated test before changing code.
Fix it safely: make the smallest change that addresses the cause, get it reviewed, and ship it through the normal pipeline or the hotfix path. Verify in production with the same logs and metrics that showed the problem. Run any data repair as a scripted, reviewed step that checks row counts.
Close the loop with a blameless write-up: timeline, root cause, why tests and monitoring missed it, and the follow-up actions (a regression test, an alert, a validation rule), each with an owner.

Follow-up

How do you decide between rolling back and fixing forward with a hotfix?
The defect does not reproduce in any test environment. What do you do next?
The bug wrote incorrect data for two days. How would you correct it safely?

Explain your understanding of page load optimization and caching.

Medium · Problem Solving & Production Scenarios

Approach

Measure before optimising. Use the browser DevTools Network waterfall and Performance panel, plus Lighthouse. Name the metrics: time to first byte for the server side, and the Core Web Vitals LCP, INP and CLS for what users experience.
Cut bytes and blocking work: minify and compress (gzip or Brotli), code-split bundles, resize images and serve them as WebP or AVIF, add loading="lazy" below the fold, mark scripts defer or async so they do not block parsing, and preload critical fonts and CSS.
Explain HTTP caching. Cache-Control: max-age with immutable suits fingerprinted assets (app.3f2a.js), and a new file name busts the cache on deploy. ETag or Last-Modified lets the browser revalidate and get a 304 Not Modified. no-cache means revalidate before use, and no-store means never store; use no-store for sensitive account pages.
Cover the other cache layers: a CDN caches static assets near users. On the server, a cache-aside layer such as an in-memory store or Redis holds expensive query results with a TTL. Indexing slow database queries lowers time to first byte when nothing is cached.
State the trade-off: caching trades freshness for speed, and invalidation is the hard part. Invalidate or delete keys when the data is written, keep TTLs short for volatile data, and mark per-user responses Cache-Control: private so a shared cache never serves one user's data to another.

Follow-up

After a deploy, users still get the old CSS and JavaScript. How do you fix that permanently?
What is the difference between Cache-Control no-cache and no-store?
A cached value goes stale when the database row is updated. How do you keep the two consistent?

If you are monitoring a system and notice a sudden spike in CPU usage, what are your first steps?

Medium · Problem Solving & Production Scenarios

Approach

Confirm and scope it. Is the spike on one host or all of them, short or sustained? Does it line up with a deploy, a traffic increase, a scheduled batch job, or a rise in latency and errors? User impact decides how urgent the response is.
Find the culprit on the host: top or htop sorted by CPU (Task Manager or Process Explorer on Windows), then top -H -p <pid> for the hot threads. Separate user time, system time and iowait (waiting on disk, not computing), and check steal time on virtual machines.
For a Java service, take several thread dumps a few seconds apart with jstack <pid> or jcmd <pid> Thread.print. Convert the hot thread ID to hex to match the nid field in the dump. Check jstat -gcutil for back-to-back full GCs, which show up as CPU when memory is short. Use JFR or async-profiler for a flame graph.
Know the common causes: a tight or infinite loop, a retry storm, catastrophic regex backtracking, N+1 queries or heavy serialisation in application code, GC thrashing, bot or crawler traffic, and a runaway batch job.
Mitigate, then fix. Capture thread dumps before any restart, because a restart destroys the evidence. Roll back a suspect deploy, scale out, or rate-limit while you fix the root cause. Then add an alert on sustained CPU and latency so the next spike is caught early.

Follow-up

CPU is high but traffic is flat. What does that rule in and rule out?
The spike is on the database server rather than the application server. Where do you look?
A restart made the spike go away. Why is that not the end of the investigation?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

What is the difference between the integer 3 and the string "3" in JavaScript?

Medium · Basic Technical Trivia

Approach

State the types: typeof 3 is "number" (a 64-bit IEEE 754 double) and typeof "3" is "string". They look the same when printed but are different values.
Explain equality. 3 == "3" is true because loose equality converts the string to a number before comparing. 3 === "3" is false because strict equality never coerces and the types differ. Say that you default to ===.
Show the operator traps: 3 + "3" gives "33" because + concatenates when either operand is a string, while 3 - "3" gives 0 and "3" * "3" gives 9 because the other arithmetic operators convert to number.
Cover explicit conversion: Number("3") or parseInt("3", 10) gives 3, and String(3) gives "3". Number("3a") is NaN but parseInt("3a", 10) is 3, and Number("") is 0. Check bad input with Number.isNaN.
Tie it to real bugs: form fields and URL parameters always arrive as strings, so summing input values without converting them concatenates them. Also note truthiness: "0" is truthy while 0 is falsy.

Follow-up

What do "5" - 2 and "5" + 2 evaluate to, and why are they different?
How would you convert a user-entered amount to a number and reject invalid input?
How does the same distinction work in Java, where "3" == 3 does not compile and == on two Strings compares references?

What is XML, and how is it typically used in software development?

Medium · Basic Technical Trivia

Approach

Define it: Extensible Markup Language, a W3C text format for hierarchical, self-describing data. Unlike HTML, it has no fixed tag set; the tag names come from the application.
Know the well-formedness rules: one root element, every tag closed or self-closing, proper nesting, case-sensitive names, quoted attribute values, and < and & escaped as < and & (or wrapped in CDATA). Separately, a document is valid when it conforms to a DTD or XSD schema.
List typical uses: configuration (Maven pom.xml, Java web.xml, Spring bean files, Android layouts), SOAP web services, data exchange between systems, RSS feeds, SVG, and the zipped XML inside .docx and .xlsx files.
Explain how Java parses it. DOM loads the whole tree into memory for random access. SAX streams events with low memory use. StAX is a pull parser. XPath queries nodes and XSLT transforms documents. Disable external entity resolution to prevent XXE attacks.
Compare it with JSON: JSON is lighter and maps directly onto JavaScript objects. XML adds attributes, namespaces, comments, mixed content and mature schema validation, which is why older enterprise integrations still use it.

Follow-up

What is the difference between a well-formed XML document and a valid one?
You have to process a 2 GB XML file in Java. Would you use DOM or SAX/StAX, and why?
When would you choose JSON over XML for a new API, and when would you keep XML?

Can you write the HTML code required to display an unordered list?

Medium · Basic Technical Trivia

Approach

Write it exactly: <ul> on its own line, then one <li>Item</li> per entry, then </ul>. Every list item goes in an li, and li elements are the only content allowed directly inside ul.
Contrast it with the related elements: ol is for ordered, numbered lists (with start and reversed attributes), and dl/dt/dd is for term-description pairs. Choose ul when the order of items carries no meaning.
Show nesting correctly: a sub-list goes inside an li (<li>Fruit<ul><li>Apple</li></ul></li>), not directly inside the outer ul.
Handle presentation in CSS, not markup: list-style-type (disc, circle, square, none) changes the bullets, and resetting padding and margin with display: flex turns the list into a horizontal nav bar. The type attribute on ul is obsolete in HTML5.
If asked to build it from data in JavaScript, use document.createElement('li') and set textContent for each item. Avoid putting user-supplied strings into innerHTML, because that opens an XSS hole.

Follow-up

How would you nest a second-level list under one item?
How would you style this list as a horizontal navigation menu with no bullets?
Write JavaScript that renders an array of strings into this list safely.

How do you go about debugging an application when you don't immediately know the root cause?

Medium · Problem Solving & Production Scenarios

Approach

Pin down the symptom exactly: the error text, the input, the environment, and whether it always happens or only sometimes. Then get a reliable reproduction and shrink it to the smallest failing case.
Ask what changed: recent deploys, configuration, dependency versions, data volume or shape, and infrastructure. Diff a working environment against the failing one, because the difference usually points at the cause.
Halve the search space on each step. Use git bisect across commits (about log2(n) builds), switch off or stub half the components, or bisect the input data. Add logging or breakpoints at component boundaries to find where actual state first departs from expected state.
Work one hypothesis at a time. For each, predict what you should observe if it is true, run one test that changes a single variable, and keep notes so you do not repeat dead ends. In a stack trace, start from the first frame in your own code.
Check the usual suspects: null or missing data, encoding and timezone differences, off-by-one errors, stale caches, and for intermittent failures, race conditions and timeouts that correlate with load. Finish with a regression test that fails before the fix and passes after it.

Follow-up

The bug happens only in production and never locally. How do you investigate?
The bug disappears as soon as you add logging. What does that suggest, and how do you proceed?
Walk through how git bisect finds the bad commit and what you need for it to work.

Suggested practice checklist

The plan starts with the loop and your logistics answers, then covers the skills-test languages, spoken fundamentals, production scenarios and behavioral stories, and ends with a full mock panel.

Day 1 — Map the loop and settle your logistics answers

Write the three reported rounds in order (Phone Screen, Skills Test, Panel Interview) and one line on what each covers.
Write a short background walk-through that ends on why a software role for financial institutions fits you.
Write your relocation answer for Richardson and Dallas, Texas, and your work-authorization answer, each in a sentence or two.

Deliverable: A one-page loop map, a written background walk-through and written relocation and work-authorization answers.

Day 2 — JavaScript, HTML and XML fundamentals

Write ten JavaScript expressions mixing numbers and strings, predict each result and then check it in a browser console; write one paragraph on how the number 3 differs from the string "3".
Write an HTML page with a nested unordered list and a small form using label and input elements.
Write a small well-formed XML document, list the well-formedness rules, and write two sentences on where XML is used and how it differs from JSON.

Deliverable: A coercion table with verified results, an HTML file with a list and a form, and an XML example with a short usage note.

Day 3 — Java and SQL snippet tracing

Write eight short Java snippets (integer division, string == versus equals, ++ ordering, loop bounds, a finally block), predict the output of each on paper, then run them.
Write six SQL queries on a two-table schema: an inner join, a left join that finds unmatched rows, GROUP BY with HAVING, a NULL check and an ORDER BY with a limit.

Deliverable: A predicted-versus-actual output sheet for the Java snippets and six SQL queries with one sentence each describing the rows returned.

Day 4 — Recursion and the smallest value in an array, spoken

Write a one-sentence definition of recursion, then factorial and sum-of-a-list with a clearly marked base case, and trace the call stack for one input.
Write a function that returns the smallest value in an array in Java and in JavaScript, handling the empty array, and note that the loop is O(n) time and O(1) space.
Say both answers aloud, definition first and example second, and record yourself.

Deliverable: Two working smallest-value functions, a recursive example with a traced call stack and a recording of the spoken recursion answer.

Day 5 — Production scenarios: defects, debugging, CPU and caching

Write numbered step sequences for resolving a production defect and for debugging when the root cause is unknown, each ending with verification and prevention.
Write a CPU spike answer: find the host and process, compare with recent deploys and traffic, mitigate, then profile.
Write a caching and page load outline covering browser cache headers, server-side caching, compression, script loading and how stale data is invalidated.

Deliverable: Four one-page answer outlines: production defect, unknown-cause debugging, CPU spike, and caching with page load.

Day 6 — Testing and deployment answer plus STAR stories

Write a testing and deployment answer from a real change you shipped: tests, review, staging, rollout, rollback, monitoring.
Outline STAR stories for a major issue, a tight deadline and a disagreement with a lead, marking your own actions with "I" and ending each with a result.
Add the cost of each story (what slipped, what you would change) so none of them reads as flawless.

Deliverable: One deployment answer and three STAR outlines with situation, task, action and result.

Day 7 — Full mock panel

Write your resume-project story, separating what you did from what the team did, and write your why-Argo-Data answer.
Run a mock panel with two people: one fundamentals question, one production scenario, one behavioral story and the relocation question.
List the three weakest answers from the mock and rewrite them.

Deliverable: A recorded mock panel, a written resume-project story and a list of three rewritten answers.

STAR stories for the Argo Data panel

Candidates report that behavioral questions carry a large share of the loop, especially the panel. For this role the stories should show how you handled production problems, worked with a team under pressure, disagreed with a lead and still delivered, and can say plainly why you want this job and where you can work.

Tell me about a time when you had to learn a completely new technology on the fly.

How do you handle testing and deployments to ensure system stability?

Medium · Problem Solving & Production Scenarios

Approach

Answer in layers, then back it with one real change you shipped: automated tests for the changed logic plus a regression test for any bug being fixed, peer code review, a build that runs the tests, then a pass in a staging environment that resembles production.
Describe the release itself: a small change set, a rollback plan written before deploying, a quiet window if the system is busy, and smoke tests straight after release.
Cover database changes separately. Make them backward compatible so old and new code both run, run the migration apart from the code release, and keep it reversible where you can.
Say what you watch after release (error rate, response time, CPU) and what triggers a rollback. Finish with one honest example of a release that went wrong and the check you added afterwards.

Follow-up

A deployment fails halfway through. What do you do first?
How do you test a change that alters a database table?
What do you do when there is no staging environment?