Aig - American International Group Software Engineer Interview Guide

The 60-second brief

Aig - American International Group sells insurance and financial products, and the Software Engineer role builds and maintains the software behind them. Candidates describe work on customer-facing features, on internal systems that make operations more efficient, and on API, cloud and data analytics projects. The requirements candidates list name Java, C# or Python, web frameworks and cloud technologies, with .NET and React mentioned as tools.

This guide is for Software Engineer candidates preparing for the Aig - American International Group loop. It covers the four reported rounds, the coding, object-oriented, testing and API questions candidates describe, the React, SQL and MongoDB topics they report, and the behavioral prompts that fit the HR screening and the team interviews. It does not cover other roles at the company.

Candidates report four rounds over roughly three to five weeks: Initial Screening, Technical Interviews, Behavioral Assessments and Engagement with Team.

Focus areas

• Web Development with React
• SQL (Query Writing)
• SQL Aggregations

Overview

Candidates describe a Software Engineer role that combines building features with keeping existing systems healthy. Duties they list include designing and developing solutions, giving code review feedback to peers, joining sprint planning and retrospectives, and fixing production problems. The languages named are Java, C# and Python, with web frameworks, cloud technologies, CI/CD and DevOps practice also listed. Familiarity with insurance or financial-services systems and with data analytics tools is listed as a plus, not a requirement.

The reported topic mix is broad rather than deep in any one area: web development with React, SQL query writing, SQL aggregations, MongoDB aggregation operators, and technical questions drawn from your resume. The reported question pool adds object-oriented principles, choosing between data structures, optimizing code, testing and debugging, reversing a linked list, and finding two numbers in an array that add up to a target. That is a set you can cover completely in a week, so prepare each item to the level of working code or a clear spoken explanation. Cloud technologies are a listed must-have, so also be ready to describe how a service you built was deployed to a cloud platform: the compute it ran on, how configuration and secrets were handled, and the stages of its CI/CD pipeline.

Resume-based questions deserve the most care, because they can reach any line you wrote. Choose one primary language from Java, C# or Python for the coding exercises, and for every project on your resume be ready to explain what you built, which decisions were yours, what went wrong and how you tested it. The other stages candidates name are behavioral assessments and meetings with potential colleagues and managers, so prepare a small set of specific stories that can be reshaped to fit different prompts.

The Aig - American International Group interview process

These are the four stages candidates report, in the order they describe them. Details vary by team and location, so treat the format as a guide and confirm specifics with the recruiter.

Initial Screening

Candidates report an initial screening with HR that assesses fit for the role. A sensible way to prepare is a clear account of your background and the languages and projects you have worked on.

What to demonstrate

• Whether your background fits the role, as candidates describe the screening
• How clearly you describe your experience and the work you have done

How to prepare

• Write a two-minute walk through your resume, naming the languages you have shipped in (Java, C# or Python) and one project in detail.
• Prepare the answer to what you are proficient in and how you used each language, with one concrete project per language.
• Ask the recruiter which stages follow and what each covers, and note your location constraints and timeline so you can answer plainly.

Technical Interviews

The technical stage is reported as several interviews, with coding challenges possible and past projects discussed. React, SQL, MongoDB aggregation and resume-based technical questions are reported topics across the loop. Treat it as two skills: writing correct code while explaining it, and telling the story of your own projects precisely.

What to demonstrate

• Working code and clear reasoning in any coding challenge
• Command of programming languages and fundamentals such as object-oriented design and data structures
• How well you explain the technical decisions, debugging and testing in your past projects

How to prepare

• Write reverse-a-linked-list and two-sum from scratch in your chosen language, with tests for empty, single-element and duplicate inputs, and state the complexity of each.
• Prepare a spoken comparison of arrays, linked lists, hash maps, stacks, queues and trees, and a short example of the four object-oriented principles in code you have written.
• Prepare the challenging technical problem story and the API development answer, and for each resume project the testing and debugging approach you used.
• Practise SQL grouping queries, a MongoDB aggregation pipeline and a small React component that loads data, and be ready to describe how a service you built was deployed to a cloud platform and through CI/CD.

Behavioral Assessments

Candidates report a behavioral stage that looks at how you work with other people and whether you fit. Reported prompts cover working under pressure, handling feedback and adapting to change. The stories you prepare here carry over to the team round.

What to demonstrate

• How you collaborate, communicate and handle disagreement
• How you react to pressure, feedback and change on a project
• Whether your examples show decisions you made and results you can describe

How to prepare

• Write four STAR outlines (pressure, disagreement, prioritizing multiple projects, taking the lead), each with one decision of yours and one result.
• Add two short stories for handling feedback from teammates and adapting when project requirements changed.
• Rehearse aloud with a phone recording and cut any story where your own action is hard to find.

Engagement with Team

The final stage is reported as meetings with future teammates and managers. Nothing reported says what these meetings ask, so go in with your general technical and behavioral material ready, and with questions of your own for the team.

What to demonstrate

• How you come across to the colleagues and managers you meet
• How clearly you describe your own experience and working style to future teammates

How to prepare

• Prepare the motivation answer and the disagreement story so they sound conversational, not recited.
• Write a short account of how you give and receive code review feedback and how you work in a sprint.
• Prepare questions for colleagues and managers about how the team tests, deploys and handles production issues.

Where candidates lose points

Naming Java, C# and Python on the resume but being unable to describe a project in each

For every language and framework on your resume, prepare one project with your role, a design decision, a bug you fixed and how you tested it. Remove anything you cannot defend.

Reversing a linked list and losing the rest of the list, or returning the wrong head

Save the next pointer before reassigning it, return the previous node at the end, and test an empty list, one node and two nodes. State O(n) time and O(1) extra space for the iterative version.

Solving two-sum with a nested loop and stopping there, or missing duplicates and the no-answer case

Give the O(n squared) version first, then the one-pass hash map storing value to index, check for the complement before inserting the current element, and say what you return when no pair exists.

Writing a SQL or MongoDB aggregation that groups correctly but filters in the wrong place

In SQL, WHERE filters rows before grouping and HAVING filters group results. In MongoDB, a $match before $group filters documents like WHERE, and a $match after $group on the accumulated field, for example {$match: {total: {$gt: 1000}}}, plays the role of HAVING. Check a join does not duplicate rows before you count or sum.

Telling behavioral stories in 'we' with no decision of your own and no result

Structure each story as situation, task, your action, result. Name the choice you made, the alternative you rejected and a concrete outcome, then practise it aloud until it is short.

Questions & practice

Solve a coding problem that requires you to implement a specific algorithm.

Medium · Coding / Algorithms

Approach

1. Before coding, pin down the constraints: input size, value ranges, whether the input is sorted, whether duplicates or negatives appear, and what to return when there is no answer. The constraints decide which algorithm fits.
2. Match the problem to an algorithm family and state its preconditions: binary search needs sorted input and gives O(log n); BFS finds shortest paths in unweighted graphs in O(V + E); Dijkstra needs non-negative weights and runs in O((V + E) log V) with a binary heap; merge sort is O(n log n) and stable, while quicksort averages O(n log n) but degrades to O(n^2) with poor pivots.
3. Trace the algorithm by hand on a small example before writing code, then write it with clear names and state the loop invariant (for binary search: the answer, if present, is always inside [lo, hi]).
4. Test the edges aloud: empty input, one element, all duplicates, the target at either end, and off-by-one bounds such as lo <= hi versus lo < hi. Then state time and space complexity, including recursion stack space.
5. If you start with a brute force, give its complexity first and say which constraint rules it out before improving it.

Follow-up

• Can you write that iteratively instead of recursively, and what does that change for very deep inputs?
• How would you prove the algorithm is correct?
• What changes if the input is too large to fit in memory?

How would you optimize a piece of code for performance?

Medium · Coding / Algorithms

Approach

1. Measure before changing anything: reproduce the slow case, set a target (for example p95 latency or throughput), and profile to find where the time actually goes, using Java Flight Recorder or VisualVM, dotnet-trace or the Visual Studio profiler, or cProfile in Python.
2. Fix the algorithm and data structure first, since that is where the large gains are: replace a nested-loop lookup that is O(n^2) with a hash set or map for O(n), avoid repeated work with memoisation, and stop sorting inside loops.
3. Then look at I/O, which usually dominates in business applications: N+1 database queries replaced by a join or batch fetch, missing indexes found with the query plan, pagination instead of loading whole tables, and caching of read-heavy data, with a stated invalidation rule.
4. Only then tune at the micro level: fewer allocations, StringBuilder instead of string concatenation in loops, avoiding boxing in hot paths, and pooling expensive connections.
5. Prove the gain with a repeatable benchmark (JMH, BenchmarkDotNet, timeit) and keep the existing tests green. Name the cost you accepted, such as extra memory, cache staleness or less readable code.

Follow-up

• How do you tell whether the bottleneck is CPU, memory, the database or the network?
• You added a cache and now users see stale data. What do you do?
• When do you stop optimising?

Describe the differences between various data structures and when to use them.

Medium · Coding / Algorithms

Approach

1. Arrays and dynamic arrays (ArrayList, List<T>, Python list): O(1) access by index and amortised O(1) append, but O(n) insert or delete in the middle. Contiguous memory gives them good cache locality, which makes them the default choice.
2. Linked lists: O(1) insert after a node you already hold, but O(n) access by position and poor cache locality. O(1) delete of a held node needs a doubly linked list or a pointer to its predecessor; in a singly linked list without the predecessor, deleting means an O(n) search. A linked list rarely wins for general storage; ArrayDeque is the usual queue choice in Java.
3. Hash maps and sets (HashMap, Dictionary, dict): average O(1) lookup, insert and delete, with no ordering. Worst case is O(n) under heavy collisions in Python dict and .NET Dictionary, while Java 8+ HashMap turns heavily colliding buckets into trees, capping it at O(log n) for comparable keys. Balanced trees (TreeMap, SortedDictionary) give O(log n) with sorted order and range queries.
4. Stacks (LIFO) for undo, parsing and DFS; queues (FIFO) for BFS and work queues; heaps (PriorityQueue in Java, and in .NET 6+; heapq in Python) give O(log n) push and pop and O(1) peek at the minimum, which fits scheduling and top-k problems.
5. Choose from the access pattern: say which operations dominate (lookup by key, ordered iteration, min/max, insertion at the ends) and pick the structure whose costs match. Mention tries for prefix search and adjacency lists versus matrices for sparse versus dense graphs.

Follow-up

• How does a hash map handle collisions and resizing, and why does a key's hashCode/equals contract matter?
• How would you find the k largest values in a stream of n items, and at what cost?
• When would a sorted array with binary search beat a hash map?

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Clarify the node shape (singly linked, value plus next) and whether to reverse in place and return the new head. Confirm the empty-list case returns null.
2. Iterative solution with three pointers: prev = null, curr = head; while curr is not null: next = curr.next; curr.next = prev; prev = curr; curr = next; return prev. Saving next before rewiring is the step people get wrong.
3. Complexity: O(n) time and O(1) extra space, since each node is visited once and only pointers change.
4. Recursive alternative: reverse the rest of the list, then set head.next.next = head and head.next = null. It is also O(n) time but uses O(n) call-stack space, so a very long list can overflow the stack in Java or C#, or hit Python's recursion limit.
5. Test by hand on empty, one-node, two-node and three-node lists, and check that the old head's next is null afterwards so the result has no cycle.

Follow-up

• Reverse only the nodes between positions m and n, in one pass.
• Reverse the list in groups of k nodes.
• How would you check whether a linked list is a palindrome using O(1) extra space?

Given an array, find the two numbers that add up to a specific target.

Medium · Coding / Algorithms

Approach

1. Clarify the output (indices or values), whether exactly one answer is guaranteed, whether an element may be used twice, and whether the array can contain negatives and duplicates.
2. Brute force checks every pair in O(n^2) time and O(1) space. State it, then improve on it.
3. One-pass hash map from value to index: for each index i with value x, compute complement = target - x; if the complement is already in the map, return [map[complement], i]; otherwise store x -> i. That is O(n) time and O(n) space.
4. Checking for the complement before inserting the current value keeps the same element from being used twice and still handles duplicates correctly, such as [3, 3] with target 6.
5. If the array is already sorted, two pointers from both ends give O(n) time and O(1) space. If it is not, sorting first costs O(n log n) and loses the original indices unless you sort (value, index) pairs. In Java or C# with int values, note that target - x can overflow, and use a long if the ranges allow it.

Follow-up

• Return all unique pairs that sum to the target, without duplicates.
• Extend it to three numbers that sum to the target. What complexity can you reach?
• How would you solve it if the data were too large for one machine's memory?

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?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

• A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
• Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

What programming languages are you proficient in, and how have you used them in your projects?

Medium · Technical / Domain Questions

Approach

1. Lead with one or two languages you can defend in depth, not a list of ten. The requirements candidates describe for this role name Java, C# or Python, so if you know one of them well, put it first.
2. For each language you claim, tie it to one project: what you built, which part of the code you owned, and one concrete result (a latency figure, a defect count, a release you shipped).
3. Show language-level depth with one specific detail: Java generics and type erasure, or how the garbage collector affected a service; C# async/await and LINQ deferred execution; Python generators, or the GIL and why you used multiprocessing for CPU-bound work.
4. Name the ecosystem around the language: build tool (Maven/Gradle, dotnet CLI/NuGet, pip/poetry), test framework (JUnit, xUnit/NUnit, pytest) and how the code reached production through CI.
5. Rate your weaker languages honestly ('I can read and change TypeScript/React code, but my depth is in Java'). Overclaiming a language leaves you exposed on follow-up questions you cannot answer.

Follow-up

• What is one thing your main language makes hard, and how did you work around it?
• If you had to rewrite that project in another of the languages you listed, what would change?
• How do you get productive in a codebase written in a language you have not used?

Can you explain the principles of object-oriented programming?

Medium · Technical / Domain Questions

Approach

1. Name the four principles and give a one-line definition of each: encapsulation (state hidden behind methods that enforce invariants), abstraction (callers depend on what an object does, not how), inheritance (reusing and specialising a base type) and polymorphism (one call resolving to different implementations at runtime through virtual dispatch).
2. Use one running example for all four, for instance a Policy base type whose subclasses override calculatePremium(), with a private premium field that only validated methods can change. That shows each principle in code instead of reciting definitions.
3. Separate overriding (runtime, subtype polymorphism) from overloading (compile-time, same name with different parameters), and know that Java methods are virtual by default while C# needs virtual/override.
4. Cover the trade-off: deep inheritance hierarchies couple subclasses to base-class internals, so prefer composition and interfaces when the relationship is 'has a' or 'can do' rather than 'is a'. The Square-extends-Rectangle example shows a Liskov substitution violation.
5. Close with SOLID as the design rules that follow from these principles, and give one example of each principle you actually applied, such as dependency inversion through constructor injection to make a class testable.

Follow-up

• When would you choose an interface over an abstract class in Java or C#, given that both now allow default method implementations?
• Give an example where inheritance caused a problem in real code. How would composition have fixed it?
• How does encapsulation help when several threads touch the same object?

Describe a challenging technical problem you encountered and how you resolved it.

Medium · Technical / Domain Questions

Approach

1. Pick a problem with a measurable symptom (error rate, latency, data mismatch, failed deployment) where you personally did the diagnosis, and state the symptom with numbers in the first two sentences.
2. Walk through the diagnosis as a sequence of hypotheses: what you suspected first, what evidence (logs, metrics, a profiler, a minimal repro) ruled it in or out, and how you narrowed it to the root cause.
3. Explain the fix and at least one alternative you rejected, with the reason (risk, effort, blast radius). The trade-off is the technical content of the answer.
4. Show verification and prevention: the test that failed before and passed after, the metric that confirmed the fix in production, and the alert, check or code-review rule you added so it would not recur.
5. Say 'I' for your own actions and credit others precisely. A story where the team did everything and your part is unclear leaves your own contribution invisible.

Follow-up

• How did you confirm that the cause you found was the cause and not a coincidence?
• What would you do differently if the same symptom appeared tomorrow?
• How did you communicate the problem and its impact to people outside engineering?

How do you approach software testing and debugging?

Medium · Technical / Domain Questions

Approach

1. Describe testing as layers: many fast unit tests (JUnit, xUnit/NUnit or pytest) with mocks only at real boundaries such as the database, network and clock; fewer integration tests against a real database or service container; a small set of end-to-end tests for the critical user paths, all run in CI on every change.
2. Say what a good test asserts: behaviour and edge cases (empty input, nulls, boundaries, invalid data, concurrency), not implementation details, so refactoring does not break it. Coverage is a signal for untested code, not a target to hit.
3. Give a debugging sequence: reproduce the failure reliably, read the full stack trace and logs around the first error, shrink it to a minimal case, then change one variable at a time to test a hypothesis.
4. When something used to work, find what changed: compare configs and dependency versions, and use git bisect to find the commit that broke it in O(log n) builds.
5. Finish every fix with a regression test that fails before the change and passes after, so the bug cannot quietly return.

Follow-up

• How do you handle a flaky test that fails one run in twenty?
• How do you debug a problem that happens only in production and you cannot reproduce locally?
• What would you mock in a unit test for a class that calls an external API and writes to a database, and what would you leave real?

Suggested practice checklist

The plan follows the reported loop: resume and screening groundwork first, then coding, fundamentals, React, API and cloud work, SQL and MongoDB, behavioral stories, and a full run-through for the team round.

Day 1 — Map the loop and audit your resume

• Write the four reported rounds in order (Initial Screening, Technical Interviews, Behavioral Assessments, Engagement with Team) and one line on how you will prepare for each.
• List every language, framework and project on your resume and mark how deep you can go on each, from 'can explain' to 'can whiteboard'.
• Choose the primary language for coding practice from Java, C# or Python, and write your two-minute walk through your background.

Deliverable: A one-page loop map, a resume inventory with a depth rating per line, the chosen language, and a written two-minute introduction.

Day 2 — Coding core: linked list, hash map, data structures

• Write reverse a linked list iteratively and recursively, with tests for empty, single-node and two-node lists.
• Write two-sum as brute force and as a one-pass hash map, covering duplicates and no valid pair, and note the time and space of each.
• Write binary search with its loop invariant and a BFS shortest path on an unweighted graph, and state the complexity of each.
• Make a table of arrays, linked lists, hash maps, sets, stacks, queues, trees and heaps with lookup, insert and delete costs and one use case each.

Deliverable: Four working, tested functions and a one-page data-structure comparison with complexities.

Day 3 — Object-oriented design, performance, testing and debugging

• Explain encapsulation, abstraction, inheritance and polymorphism with one small example in your language, such as an interface with two implementations.
• Take a slow function that uses nested loops, measure it, optimize it and record the before and after timings.
• Write your debugging sequence for a failing application (reproduce, read logs, narrow with a bisect, fix, add a regression test) and add unit tests for your day-two functions.

Deliverable: An OOP example, a measured before-and-after optimization, a written debugging checklist and a test file.

Day 4 — React, API development and cloud deployment

• Build a small React component that fetches data and handles loading, error and empty states. Cover useState and useEffect, dependency arrays and cleanup so stale responses are ignored, list keys, lifting state, controlled forms, and when useMemo or useCallback help.
• Design a REST API for an insurance entity: /policies and /claims, a claim-status transition, idempotent claim submission, validation, an error format and pagination over a policyholder's claims.
• Describe how a service you built was deployed to a cloud platform: compute, configuration and secrets, and the CI/CD pipeline stages from commit to release.
• Prepare and say aloud a two-minute answer about your API development experience and the technologies you used.

Deliverable: A working component, an endpoint table for policies and claims, a written deployment walk-through and a recorded API answer.

Day 5 — SQL and MongoDB aggregation

• Write grouped queries with COUNT, SUM, HAVING and a top-N per group, and check that your joins do not duplicate rows.
• On an insurance-shaped claims collection, write MongoDB pipelines with $match, $group ($sum, $avg, $push, $addToSet), $project, $unwind, $lookup and $sort, for example claims per policy type with an average payout.

Deliverable: Five verified SQL queries with checked outputs, notes on the two practice questions and a set of working aggregation pipelines over the claims collection.

Day 6 — Behavioral stories

• Write STAR outlines for working under pressure, a team disagreement, prioritizing several projects and a project you led.
• Add short stories for receiving team feedback and adapting to a change in project direction.
• Say each story aloud, cut it to its essentials and record it so you can check that your own decision and the result are clear.

Deliverable: Four STAR outlines, two shorter stories and one recorded run of all of them.

Day 7 — Full run-through for the team rounds

• Answer aloud the languages question, the challenging technical problem story and the motivation question as if speaking to a future colleague.
• Prepare questions for colleagues and managers about how the team tests, reviews code, deploys and handles production issues.
• Do one more mock pass on your weakest area from days two to five.

Deliverable: Recorded answers to three questions, a list of questions for the team and a note on the weakest area to review.

Behavioral and team-round stories

Candidates report behavioral assessments that look at interpersonal skills and cultural fit, followed by meetings with potential colleagues and managers. The reported prompts are about pressure, disagreement, motivation, prioritization and leading a project. Prepare stories where your own decision is visible, and be ready to say what you would do differently.

1. Describe a time you had to adapt to a significant change in a project.

What is your experience with API development, and what technologies have you used?

Medium · Technical / Domain Questions

Approach

1. Choose one API you built or extended and name the real stack: language, framework and data store, for example Java, C# with .NET, or Python. Do not list tools you only touched once.
2. Describe the contract: resources and HTTP verbs, status codes, request validation, error response shape, authentication, and how you handled pagination and versioning.
3. Cover reliability: idempotent PUT and DELETE, an idempotency key for retried POSTs, timeouts and what happens when a downstream call fails.
4. Say how you tested it (unit tests, integration tests against a test database, contract checks) and how it shipped through CI/CD, with one measurable outcome such as lower latency or fewer client errors.
5. If you have consumed APIs more than built them, say so and describe how you handled retries, rate limits and malformed responses from the provider.

Follow-up

• How would you change an endpoint's response without breaking existing clients?
• What happens if a client retries a create request after a network timeout?
• How did you document the API and keep the documentation accurate?

Describe a situation where you had to work under pressure. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Pick a real situation with a hard constraint, such as a production incident, a fixed release date or a sudden scope change. State it in two sentences, then move to what you did.
2. Show triage: what you checked first, what you chose not to do, and what you told stakeholders and when. Calm, ordered decisions are the point of the story.
3. Give the result with a number or concrete outcome (service restored, release shipped, defect rate), then one thing you changed afterwards, such as an alert, a test or a runbook.
4. Avoid a story that is only long hours or heroics. A line like 'I cut the scope to the two features that blocked the release and told the manager the rest would ship next sprint' shows judgment.

Follow-up

• What did you decide to drop, and who agreed to it?
• How did you keep quality up while moving fast?
• What would you set up so the next incident is less stressful?

Can you provide an example of a time when you had to navigate a disagreement within your team?

Medium · Behavioral / Leadership

Approach

1. Choose a disagreement about substance, such as a design choice, a review comment or a deadline estimate, and state both positions fairly so the other person does not look like a villain.
2. Describe how you moved it forward with evidence: a small prototype, a benchmark, a written pros and cons list, or a conversation with the person who owned the decision.
3. Say who made the final call, what you did if it was not your option, and how the working relationship looked afterwards.
4. Avoid stories where you were simply right. The strongest version includes something you learned or changed your mind about.

Follow-up

• What if the other person still disagreed after you showed the evidence?
• Would you handle it differently if the person were your manager?
• Did the decision turn out to be the right one?

What motivates you to work in the software engineering field?

Medium · Behavioral / Leadership

Approach

1. This is not a STAR question. Give a short origin (what first drew you to building software), then a pattern across your recent work that shows what you enjoy, such as tracing a bug to its cause, shipping something users touch or making a slow system faster.
2. Back each claim with a specific project, so the answer reads as evidence about you and not as a slogan.
3. Connect it to the role using only what you know: candidates describe feature work, code review, Agile teams and production troubleshooting, so say which of those you do best and why.
4. Avoid generic lines about loving problems or helping people, and avoid leading with salary or perks.

Follow-up

• What part of the job do you find least motivating, and how do you handle it?
• What would keep you motivated if you were assigned maintenance work for a year?
• Which project of yours are you proudest of, and why?

How do you prioritize your tasks when handling multiple projects?

Medium · Behavioral / Leadership

Approach

1. Show a repeatable method: list every commitment, rank by user or business impact and deadline, mark dependencies and blockers, then confirm the ranking with whoever owns each request.
2. Use one concrete example with three real tasks, for instance a production bug, a feature due this sprint and a code review queue. Say what you did first, what you deferred and how you told people.
3. Name the tools you use (a sprint board, a daily plan, estimates) and how you re-plan when something urgent arrives mid-sprint.
4. The mistake to avoid is 'I work hard and multitask'. A good answer includes a moment when you said no, or negotiated scope or dates, and what happened.

Follow-up

• Two managers each say their task is the top priority. What do you do?
• How do you handle a production issue that lands in the middle of a sprint?
• How do you decide what to estimate again after the plan changes?

Describe a project where you took the lead. What was the outcome?

Medium · Behavioral / Leadership

Approach

1. Pick a project where you set direction or coordinated others, even without the title. State the goal, the team size and what was at stake in two sentences.
2. Spend most of the answer on your actions: how you split the work, which technical or scheduling decisions were yours, how you unblocked teammates and how you ran check-ins or reviews.
3. Report the outcome honestly with something measurable (delivered date, performance gain, defects, adoption). If it went only partly well, say what you would change.
4. Use 'I' for your decisions and 'the team' for shared work, so your own contribution is clear.

Follow-up

• What was the hardest decision you made as the lead?
• How did you deal with a teammate who was falling behind?
• What would you do differently if you ran the project again?