Interview guides · AIG Claims

AIG Claims Software Engineer Interview Guide

AIG Claims Software Engineer interview guide: two reported rounds, coding, debugging, API, security and behavioral questions, plus a 7-day prep plan.

The 60-second brief

Candidates describe the Software Engineer role at AIG Claims as building and maintaining the software that moves a claim through processing and shapes the customer's experience of it. Reported projects include new features for claims processing systems, improvements to existing applications, API integrations and data analytics, with machine learning work mentioned as a possibility. Engineers work alongside product managers, designers, operations staff and other engineers, so the job mixes writing and reviewing code with turning business requirements into working software.

This guide is for candidates interviewing for the Software Engineer role at AIG Claims, including entry-level applicants. It covers the two reported rounds, every question candidates report (coding, debugging, API and security, data analysis, system design and behavioral), the technologies the role lists, and a seven-day plan to rehearse them.

Candidates report two rounds across roughly 2-4 weeks. The first is an Initial Screening, a recruiter conversation. The second is Technical Interviews, one session or several, which reports say can touch coding, system design and behavioral topics.

Focus areas

Project-based technical discussion
Memory leak debugging
Programming languages (general)

Overview

The reported work is claims software: features for claims processing systems, tuning applications that already exist, and API integrations. Candidates describe the requirements as one general-purpose language (Java, C# and Python are the examples given), web framework experience, and a firm grasp of databases and SQL. Cloud platforms such as AWS or Azure, reporting and analytics tools, Agile practice and DevOps are listed as extras. Prepare in the language you know best, and be ready to defend your choice of it.

The question list is broad rather than deep in any one area. Candidates report questions about languages you are comfortable with, a complex project you built, application security, API development and integration, and debugging a production issue. They also report a design question about a new feature for a claims management system, a question about analysing a dataset, one about making a major technical decision, and three coding questions: reverse a linked list, implement a sorting algorithm and justify it, and an array problem in LeetCode style. Topics candidates mention beyond that list are memory leak debugging, MongoDB query operators and questioning driven by your resume.

Because resume-driven and project-based questioning is reported, the cheapest preparation with the best return is knowing your own projects cold: the architecture, the data model, the failure you debugged, the trade-off you would reverse. Pair that with a small set of coding patterns you can write without hesitation, a repeatable debugging sequence, and a habit of saying why you chose a structure or approach, since several reported questions ask you to explain a choice.

Domain context helps without needing to be deep. Claims software deals with records that change state over time, documents, payments and audit needs, so when you answer the design and API questions, think in terms of claim status transitions, idempotent updates, access control and traceability. Keep that as a framing device for your own reasoning rather than a claim about how AIG Claims builds its systems.

The AIG Claims interview process

Candidates report a recruiter Initial Screening about background and fit, then Technical Interviews. Reported material includes coding (linked list, sorting, arrays), debugging and memory leaks, MongoDB query operators, SQL from the listed requirements, and behavioral questions. Resume-driven questioning is a reported topic, though no stage is named for it.

Initial Screening

Candidates describe the Initial Screening as a preliminary conversation with a recruiter about your background and how it fits the role. Candidates also report resume-driven questioning as a topic, without naming the stage it comes in, so be ready for it here and later. Treat it as a rehearsal of your own history: a coherent account of what you built, in which languages, and what you want from a claims-focused engineering role.

What to demonstrate

Candidates report that this stage looks at your background and fit for the role
Listed requirements your background will be read against: one general-purpose language, web frameworks, databases and SQL
Reported nice-to-haves to be able to speak to: cloud platforms such as AWS or Azure, Agile and DevOps practice

How to prepare

Write a one-page inventory of each resume project: purpose, your part, languages and frameworks, database, one hard bug, one decision you would revisit
Be ready to explain why you are most comfortable with your main language and where it is a poor fit
Prepare a short, honest reason for wanting claims or insurance software, drawn from your own experience rather than from guesses about the employer
Name AWS or Azure and one concrete use, for example: I deployed a Python API to AWS Elastic Beanstalk, kept its settings in environment variables and rolled back through the console. Have one similar sentence ready for SQL and for Agile

Technical Interviews

Candidates describe the Technical Interviews as one or more sessions that can include coding, system design and behavioral material. Prepare the question types candidates report: coding on a linked list, sorting and arrays; design and decision-making about a claims feature; API integration and application security; and debugging, where memory leaks are specifically mentioned. Candidates also list MongoDB query operators as a topic, without naming the stage. Talk through your reasoning aloud as you work, because several reported questions ask you to explain a choice.

What to demonstrate

Candidates report coding questions on linked lists, sorting and arrays
Candidates report a design question about a claims management feature, plus API and security questions
Candidates report debugging a production issue, with memory leak debugging among the topics
Reported topics also include project-based discussion and MongoDB query operators

How to prepare

Write linked-list reversal both iteratively and recursively, plus one sort from scratch (merge sort or quicksort) and one array problem solved with a hash map or two pointers
Outline a claims feature design: intake, claim status model, document handling, notifications, audit trail, and the API that other systems call
Rehearse a debugging sequence for a production issue, then for a service whose memory grows steadily until it restarts
Practise a handful of MongoDB queries with comparison, logical and array operators, and keep SQL joins and aggregates sharp

Where candidates lose points

Answering the linked-list, sorting or array question with code that works only for the happy path

Before coding, say the input shape and the edge cases out loud: empty list, single node, duplicates, already-sorted input, negative values. State time and space complexity, and walk your code through a tiny example to confirm the pointers.

Giving a textbook sorting answer without justifying the choice the question asks for

Name the algorithm and the deciding properties: stability, extra memory, worst case, nearly-sorted input. For example, merge sort for stable guaranteed O(n log n) with extra space, quicksort for in-place average speed, and the language's built-in sort as the practical default.

Describing a resume project vaguely and failing when questioning follows your own words

For every project on your resume, rehearse the architecture, the database schema, the hardest bug and one trade-off. Drop anything you cannot discuss in detail. Resume-driven questioning is a reported topic, so each line is fair game.

Treating the production-debugging and memory leak questions as a guessing game

Follow a fixed sequence: confirm the symptom and when it started, check what changed, read logs and metrics, reproduce, then narrow. For a leak, watch heap growth over time, take heap snapshots at two points, compare which object types grew, and trace what retains them.

Answering the claims-feature design question with a database diagram and nothing else

Start from users and requirements, then cover the claim status model, API contracts, validation and access control, audit history, and failure handling such as retries that must not duplicate a claim update. Say what you would build first and what you would postpone.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

Iterative three-pointer reversal: prev = None, curr = head; in the loop, save nxt = curr.next, set curr.next = prev, then move prev = curr and curr = nxt; return prev. O(n) time, O(1) extra space.
Recursive version, base case first: if head is None or head.next is None, return head. Otherwise set new_head = reverse(head.next), then head.next.next = head and head.next = None, and return new_head. Every level must pass that same new_head back up unchanged; returning head instead hands back the wrong node, and skipping the None check crashes on an empty list.
The recursive form uses O(n) call-stack space, and in Python a long list can exceed the default recursion limit of 1000, so the iterative form is safer.
Edge cases: empty list, single node, two nodes. Confirm the old head's next ends as None so no cycle is left behind.
Test by walking the result and comparing it with the expected sequence, and trace a three-node list on paper before running it. Know the variants: a doubly linked list, reversing between positions m and n, and reversing in groups of k.

Follow-up

Reverse only the nodes between positions m and n, in one pass.
Reverse the list in groups of k, leaving any remainder shorter than k as it is.
How would you check the list has no cycle before reversing it?

How would you implement a sorting algorithm? Explain your choice.

Medium · Coding / Algorithms

Approach

Ask about the data first: size, whether it is nearly sorted, whether stability matters, memory limits, and the key range. Those decide the algorithm.
Merge sort: O(n log n) in every case, stable, O(n) extra memory for arrays. It suits linked lists and external sorting, and I would implement it as a split, recurse, and a merge with two pointers that takes from the left side on ties to stay stable.
Quicksort: O(n log n) on average but O(n^2) worst case with bad pivots, in place, not stable; a random or median-of-three pivot makes the worst case unlikely. Heapsort guarantees O(n log n) with O(1) extra space but is not stable.
Insertion sort is O(n^2) in general but O(n) on nearly sorted input, which is why hybrids use it for small subarrays. Counting or radix sort runs in linear time when keys are small integers (counting sort is O(n + k)).
Know the library defaults: Python's sorted and list.sort use a stable Timsort-family merge sort; Java's Arrays.sort uses dual-pivot quicksort for primitives and stable TimSort for objects; C#'s Array.Sort uses an unstable introsort.

Follow-up

How would you sort data that does not fit in memory?
Why does stability matter? Give a case where it changes the result.
How do you stop quicksort degrading to O(n^2) on already-sorted input?

Can you solve this LeetCode-style problem related to arrays?

Medium · Coding / Algorithms

Approach

Clarify the input before coding: is it sorted, can it hold negatives or duplicates, how large is it, can I modify it in place, and do I return indices or values. Then state the brute force and its complexity.
Use a hash map for complement or count lookups (two-sum in O(n) time and O(n) space), and prefix sums with a map of prefix-sum counts to count subarrays summing to k, which also works with negative numbers.
Use two pointers on sorted arrays (pair sums, in-place dedupe, merging) and a sliding window for the longest or shortest contiguous subarray under a constraint. Note that a sum-based window is only valid when all elements are non-negative.
For maximum subarray sum use Kadane's algorithm: cur = max(x, cur + x), best = max(best, cur). Initialise both from the first element so an all-negative array is handled. O(n) time, O(1) space.
Before calling it done, check edge cases: empty array, one element, all duplicates, and integer overflow on sums in Java or C#. Walk through a small example by hand.

Follow-up

Can you do it with O(1) extra space?
What changes if the array arrives as a stream you cannot store?
Why does the sliding-window approach break when the array contains negative numbers?

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?

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

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.
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.
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.
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?

What programming languages are you most comfortable with, and why?

Medium · Technical / Domain Questions

Approach

Name one primary language and back it with evidence: what I shipped in it, which version and libraries, and how long I have maintained code in it. If it is Java, C# or Python, say so, since those are the languages candidates list in the role requirements.
Explain the 'why' with language properties, not taste: static typing and compile-time checks in Java and C# versus faster iteration and dynamic typing in Python, and the ecosystem each one gives for web services and data work.
Show depth with one sharp detail: Python evaluates a mutable default argument once at definition time; Java HashMap keys need consistent equals and hashCode; C# releases unmanaged resources through IDisposable and a using block.
Connect the runtime to memory: the JVM and .NET use tracing garbage collectors, CPython uses reference counting plus a cycle collector, and all of them still leak when something keeps a reference alive (static collections, caches with no eviction, event handlers never unsubscribed).
Name a language I am weaker in and say concretely how I would ramp up on it, rather than claiming equal fluency in everything.

Follow-up

How does garbage collection work in your main language, and how can a program in it still leak memory?
When would you pick Python over Java or C# for a service, and when the reverse?
What does the CPython GIL prevent, and how do you get CPU parallelism despite it?

Can you describe a complex software project you've worked on?

Medium · Technical / Domain Questions

Approach

Open with the problem and its constraints (users, data volume, latency or correctness requirement), then sketch the architecture in a handful of components before going deeper.
Say precisely which parts I designed, wrote or owned, and keep that separate from what the rest of the team built.
Pick one hard technical decision and lay out the alternatives and the trade-off, for example a synchronous API call versus a message queue, or a relational schema versus a document store, and why the chosen one fit the constraints.
Be ready to drop to code level on any component: the data model, the main queries and their indexes, error handling, and how it was tested.
Close with a measurable outcome I can defend and one thing that broke or that I would change, such as a production incident and its fix.

Follow-up

What was the hardest bug in that project, and how did you find it?
If traffic or data grew tenfold, which component would fail first and why?
Why that database or framework rather than the obvious alternative?

How do you ensure the security of your applications?

Medium · Technical / Domain Questions

Approach

Walk the layers in order: validate input on the server with allow-lists, use parameterized queries (bind parameters) against SQL injection, encode output against XSS, and enforce TLS in transit.
Separate authentication from authorization: check on every request that the caller may access that specific record, so changing an id in a URL cannot expose someone else's data (insecure direct object reference).
Protect secrets and sensitive data: no credentials in code or repos (use a secrets manager or injected environment config, with rotation), least-privilege database accounts, encryption at rest, and logs that exclude personal data.
Store passwords only as salted slow hashes (bcrypt, scrypt or Argon2), never as reversible encryption or a single fast hash such as SHA-256.
Make it a process: dependency scanning for known CVEs, static analysis in CI, security items in code review, OWASP Top 10 as a checklist. For MongoDB, reject raw JSON in filters so a client cannot inject operators such as {"$ne": null} into a login query.

Follow-up

How do you stop a user from reading another user's record by editing the id in the request?
How does a parameterized query prevent SQL injection, and where can it not help, such as a dynamic ORDER BY column?
Where do you keep API keys and database passwords, and how would you rotate one without downtime?

What is your experience with API development and integration?

Medium · Technical / Domain Questions

Approach

Describe a concrete API I built or integrated: its resources, HTTP methods and status codes (201 on create, 400 for validation, 401 versus 403, 404, 409 for conflicts, 429 for rate limits) and how it was versioned.
Handle retries safely: GET, PUT and DELETE are idempotent by HTTP semantics and POST is not, so a create endpoint takes an idempotency key and returns the original result when the same key arrives again.
For third-party integrations: set a timeout on every call, retry only retryable failures (timeouts, 5xx, 429) with exponential backoff and jitter, add a circuit breaker, and handle the partner's pagination and rate limits.
Cover auth and contracts: OAuth 2.0 client credentials or API keys for service-to-service calls, an OpenAPI spec, contract tests, and a mocked partner in automated tests.
For long-running work, return 202 Accepted with a status resource, or deliver results by webhook and verify the webhook signature before trusting the payload.

Follow-up

The caller times out and retries, but the first request actually succeeded. What happens?
How would you change a response field without breaking existing clients?
How do you test and run against a partner API that is slow or frequently down?

How would you approach designing a new feature for our claims management system?

Medium · Problem-Solving / Case Studies

Approach

Clarify before designing: which users the feature serves, the problem it solves, and the functional and non-functional requirements (audit trail, permissions, availability). State a concrete example as an assumption, such as letting a claimant upload documents to an open claim and track its status.
Model the claim lifecycle as an explicit state machine (for example opened, under review, approved or denied, paid, closed) with allowed transitions enforced on the server and every transition written to a status-history table.
Lay out components: API endpoints, file bytes in object storage with metadata in the database, a queue for slow steps (virus scan, text extraction), and notifications when the status changes.
Cover correctness: per-claim authorization, idempotent submissions, and optimistic locking with a version column so two people editing the same claim cannot overwrite each other.
Plan the rollout: feature flag, schema migration that keeps old records valid, and the metrics that will show whether the feature worked.

Follow-up

Two users update the same claim at the same time. How do you prevent a lost update?
How would you keep an audit history that cannot be silently altered?
How would you migrate existing claims onto the new status model without downtime?

If given a dataset, how would you analyze it to extract meaningful insights?

Medium · Problem-Solving / Case Studies

Approach

Start from the question and the decision it feeds, and define the metric precisely before touching the data.
Profile first: row counts, schema and types, null rates, duplicates, value ranges, date coverage, and the grain (what one row represents).
Clean deliberately and write down each choice: dedupe on the business key (ROW_NUMBER() OVER (PARTITION BY key ORDER BY updated_at DESC) and keep row 1), decide per column how to treat missing values, and check whether outliers are errors or real.
Explore with SQL or pandas: group by segments, trends over time, and distributions. For skewed values such as amounts, report the median and percentiles, not just the mean.
Validate before concluding: check sample sizes, look for confounders, and do not present correlation as causation. Finish with one clear chart, a recommendation, and its caveats.

Follow-up

How would you find and remove duplicate records in SQL?
A key column is missing in a large share of rows. What do you do?
Why might the mean mislead you on this data, and what would you report instead?

Describe your thought process for making a critical technical decision.

Medium · Problem-Solving / Case Studies

Approach

Frame the decision: the requirement, the constraints (deadline, team skills, cost, compliance) and how reversible it is. A one-way decision gets far more scrutiny than one that is cheap to undo.
List two or three real options against explicit criteria, and replace opinion with evidence where possible: a small prototype, a benchmark, or a load test.
Weigh failure modes and long-term cost: operational burden, migration path, security impact and lock-in, not just build effort.
Write the decision down (an architecture decision record covering the context, the options, the choice and why the others were rejected) and have the people it affects review it.
Define in advance the signal that would show the decision was wrong, and check it after shipping. Illustrate the whole process with one real decision I made.

Follow-up

Tell me about a technical decision that turned out wrong. What did you do?
How do you decide when the team is split and the data does not settle it?
How do you decide quickly when there is no time to prototype?

Describe how you would approach debugging a production issue.

Medium · Technical / Domain Questions

Approach

Triage before root cause: scope the impact (which users, endpoints, since when) and mitigate first if customers are affected: roll back, turn off a feature flag, or scale out.
Correlate with change: recent deploys, config edits, traffic shifts and dependency upgrades. Then use logs, metrics and traces tied together by a request or correlation id to narrow the failing path.
Form one hypothesis at a time and reproduce it with the failing input in a lower environment. Narrow regressions by bisecting commits (git bisect) or config.
For a suspected memory leak: confirm the heap retained after full GC keeps growing, take two heap snapshots some time apart and diff the largest retainers (Java heap dump via jmap and Eclipse MAT, Python tracemalloc snapshot compare_to, .NET dotnet-gcdump). Usual culprits: unbounded caches or static maps, listeners never removed, unclosed connections or streams, ThreadLocals in pooled threads.
Close the loop: fix plus a regression test, an alert that would have caught it earlier, and a blameless postmortem with follow-up actions.

Follow-up

How do you tell a real leak from a heap that is sized too small or a cache that is still warming up?
It only happens in production and you cannot attach a debugger. What do you do?
How would you make sure the same issue cannot quietly return?

Suggested practice checklist

The plan follows the reported question types in order: projects and languages, coding, debugging, APIs and security, data and SQL, design and decisions, then behavioral stories and a full mock. Each day ends with something you can reread before the interview.

Day 1 — Resume inventory and language fluency

List every resume project with its purpose, your role, languages, framework, database and one real bug, then cut or prepare anything you could not discuss in detail.
Write a spoken answer about the programming language you know best and why, including two situations where you would pick something else.
Pick your best complex project and write its story: problem, architecture, a decision you made, what went wrong and what you would change.

Deliverable: A one-page project inventory, a short language answer, and a written story for your most complex project.

Day 2 — Coding: linked list, sorting and arrays

Reverse a singly linked list iteratively, then recursively, and note time and space for each; test with empty, one-node and two-node lists.
Implement merge sort and quicksort from scratch, then write a short comparison covering stability, memory, worst case and nearly-sorted data.
Solve three array problems with a hash map or two-pointer technique, stating the brute force first and then the improvement.

Deliverable: Working code for list reversal and two sorts, a one-paragraph sort-choice explanation, and three solved array problems with complexity notes.

Day 3 — Debugging production issues and memory leaks

Write your debugging sequence for a production issue: symptom, recent changes, logs and metrics, reproduction, hypothesis, fix, verification.
For your main language, list the tools for finding a memory leak (heap dumps, profilers or allocation tracking) and the pattern for diffing two snapshots.
Describe three common causes of leaks, such as unbounded caches, listeners never removed, and growing static collections, and how you would confirm each.

Deliverable: A one-page debugging checklist plus a spoken answer to the production-issue question.

Day 4 — API development, integration and application security

Describe an API you built or consumed: contract, authentication, error handling, retries, versioning, and what happened when the other side failed.
Write a security checklist for a web application: input validation, parameterised queries, authentication and session handling, authorisation checks, secrets management and dependency updates.
Explain how you would make an update call safe to retry, using an idempotency key, and say what the caller sees on timeout.

Deliverable: A written API-experience answer and a security checklist you can speak from.

Day 5 — Data: SQL, MongoDB operators and dataset analysis

Work through both SQL practice questions in this guide (joins that fan out, and gaps found with window functions), checking row counts after each join.
Build a small claims-like collection (claim id, status, amount, adjusters, an array of notes) and write ten queries with $gt, $in, $ne, $and, $or and $exists. Include $elemMatch versus dotted conditions on an array: dotted conditions can each match a different element, $elemMatch needs one element to satisfy all of them.
Write an aggregation on the same collection with $match, $group and $sort, and one $lookup into a second collection; compare each query to its SQL equivalent.
Write the steps you would take with an unfamiliar dataset: schema and types, missing values, duplicates, distributions, outliers, a question to answer, and a chart or table that answers it.

Deliverable: Solved SQL practice questions, a sheet of ten MongoDB queries and one aggregation with SQL equivalents, and a dataset-analysis checklist.

Day 6 — Design a claims feature and explain a technical decision

Outline a feature for a claims management system: users, requirements, claim states and transitions, data model, API, notifications, audit trail and failure handling.
Add the non-functional pieces: access control for sensitive records, retries without duplicate updates, and how you would test and roll out the feature in stages.
Write a decision story from your own history using a structure of options, criteria, evidence, the choice, and what happened afterwards.

Deliverable: A one-page feature design with an API sketch and a written decision story.

Day 7 — Behavioral stories and a full mock

Write STAR notes for the five reported behavioral prompts, each with one specific decision of yours and one measurable result.
Say each answer aloud, recording it, and cut anything that is team narration without your own action.
Run a full mock: a resume walkthrough, one coding problem, one debugging question and two behavioral prompts, then list the three weakest moments and fix them.

Deliverable: Five STAR outlines, a recording of the spoken answers, and a three-item fix list from the mock.

Behavioral stories for the AIG Claims loop

Candidates report five behavioral questions: a hard situation you handled, how you prioritise across projects, working with a difficult colleague, what motivates you, and how you take feedback. Reports also say the Technical Interviews may include behavioral assessments. For a Software Engineer, strong stories are about engineering work, such as an incident, a deadline clash or a code review, and show your own decision and its result.

Tell me about a time you faced a significant challenge at work. How did you handle it?

Medium · Behavioral / Leadership

Approach

Choose a challenge that was technical or delivery-related, such as a production incident, a stalled migration or a deadline that moved, and one where you made the key decision rather than observed it.
Open with two sentences of situation and stakes, then spend most of the answer on what you considered, what you chose, and what you ruled out.
Use first-person verbs: I diagnosed, I put an alternative to the team, I wrote the rollback plan. Say plainly which parts belonged to others.
Close with a measurable result (error rate, processing time, tickets closed, delivery date kept) and one thing you would change next time.
The mistake that sinks it is a story with no obstacle, a team-wide 'we' with no individual choice, or an ending that stops at 'it worked out'.

Follow-up

What would you do differently if the same situation came up again?
How did you know your fix, rather than something else, produced the improvement?
Who disagreed with your approach, and how was that settled?

How do you prioritize tasks when working on multiple projects?

Medium · Behavioral / Leadership

Approach

Give the method first in one sentence: I rank by impact and deadline, check what blocks others, and then confirm the order with whoever depends on the work.
Anchor it in a real week where two commitments collided, for example a feature deadline against a production bug, and name both items.
Show the trade-off: what you deferred, how you told the product manager or teammate, and what you offered in exchange (smaller scope, a later date, a handover).
Mention the tools you used to keep the order visible, such as a ticket board or a written daily list, and how you re-ranked when something urgent arrived.
Avoid answers that say you simply work longer hours or that everything gets done; the answer must include something that was consciously postponed.

Follow-up

What do you do when two stakeholders each insist their task comes first?
Tell me about a time you got the order wrong and what it cost.
How do you protect time for code review and testing when deadlines tighten?

Describe a situation where you had to collaborate with a difficult team member.

Medium · Behavioral / Leadership

Approach

Describe the friction by observable behaviour (late reviews, dismissing suggestions, changing requirements without telling anyone), not by labelling the person.
Show that you went to the person first, privately, and asked about their constraints before pushing your own view.
Describe a concrete working agreement you reached, such as review turnaround, who owns which module, or a design decision recorded in writing, and note that you brought in a lead only after direct conversation had failed.
State the result for the work: delivered on time, fewer review rounds, a cleaner interface between your two parts.
The mistake is a story that casts you as the hero and the colleague as the villain; admit what you misread or adjusted on your side.

Follow-up

What did you learn about your own working style from that situation?
What would you have done if the conversation had not improved things?
How do you keep a working relationship going with someone you disagree with technically?

What motivates you to succeed in your role as a Software Engineer?

Medium · Behavioral / Leadership

Approach

Name one or two real motivators and prove each with a past example: for instance, the satisfaction of tracing an obscure bug to its cause, or shipping something that other people then relied on.
Link the motivator to behaviour a team can observe: you volunteer for the hard bug, you write the test that proves the fix, you follow a release through to production.
Tie it to the work candidates describe for the role, in your own terms: software that moves records through a business process, integrations between systems, and code that other engineers review.
Be concrete about what keeps you going when the work is dull, such as an on-call fix or a refactor, and how you handle it.
The mistake is a generic 'I love coding' or a pay-only answer; an answer with no story behind it is indistinguishable from a rehearsed line.

Follow-up

Tell me about a task that did not motivate you and how you got it done anyway.
What kind of work drains you, and how do you manage it?
How has what motivates you changed since your first role or project?

How do you handle feedback and criticism?

Medium · Behavioral / Leadership

Approach

Pick one specific piece of feedback, ideally from a code review or design review, that you initially disagreed with or found uncomfortable.
Show the sequence: you listened, asked a clarifying question, checked the claim against evidence (a benchmark, a test, the style guide), then acted on it.
Describe the behaviour change that lasted, such as adding tests before opening a pull request or writing shorter, single-purpose commits, and how you confirmed it held.
Add the other direction briefly: how you give feedback to a peer, with a specific example, a reason and a suggestion.
The mistake is claiming you never receive criticism, defending yourself in the story, or choosing a trivial piece of feedback with no consequence.

Follow-up

Tell me about feedback you received that you decided not to follow, and why.
How do you respond when feedback comes in public, such as a review comment on your pull request?
How would you give hard feedback to a teammate who is senior to you?