Interview guides · Allstate Insurance

Allstate Insurance Software Engineer Interview Guide

Allstate Insurance Software Engineer interview guide: reported rounds, OOP, coding and design questions, STAR answers and a 7-day prep plan.

The 60-second brief

Allstate Insurance is an insurance company, and candidates describe the Software Engineer role as building and maintaining applications for customers and for internal processes. Candidates also mention working alongside product management, QA and customer service, with cloud computing and data analytics as areas the work can reach. Candidate reports do not name specific systems, so choose your own insurance-style examples and use them to ground your answers: a slow quote page for the optimization question, a claim showing the wrong status for the bug question, premium payments for the transaction design, and a Coverage or Policy class for the OOP question.

This guide is for candidates interviewing for a Software Engineer position at Allstate Insurance. It covers the two reported rounds, every reported question (C++ versus Java, object-oriented programming, a project challenge, slow-application tuning, bug diagnosis, reversing a linked list, binary search, high-volume transaction design and high-availability database design), the two reported behavioral questions, and a seven-day plan. It does not cover roles outside software engineering.

Candidates report two rounds over roughly two to four weeks: a screening call with HR, then interviews with technical leads or hiring managers that mix behavioral and technical questions. The exact shape can differ by team or location.

Focus areas

Object-Oriented Programming (OOP)
Behavioral Interviewing
Pair Programming

Overview

Candidates describe the day-to-day work as building and maintaining applications, taking part in code reviews, converting input from product managers and UX designers into technical requirements, and testing and debugging. For skills, they list Java, C++ or Python, data structures and algorithms, and design principles; AWS or Azure, React or Angular, Agile and DevOps are described as bonuses. Preparation should therefore be broad rather than deep in one niche: solid fundamentals in one language, clean object-oriented design, careful debugging, and the ability to explain your reasoning.

The reported questions follow a pattern. Language and OOP fundamentals come first: the differences between C++ and Java, and what object-oriented programming means in practice. Next come problem-solving scenarios, namely tuning a slow application and diagnosing a user-reported bug. Coding is light and classic, reversing a linked list and explaining binary search. Design is stated at a high level: a system handling millions of transactions per day, and database design for high availability. Candidates also mention object-oriented programming, classes, objects and pair programming, so practise writing code while narrating your choices.

Candidates report behavioral questions about working under pressure, prioritising across several deadlines, and describing a project challenge you overcame. They are also pointed toward STAR-structured answers and toward being ready to discuss past projects in detail, including collaboration with non-engineering teams and explaining technical ideas to a non-technical audience. Build a small set of stories you can bend to fit several of these prompts, and have one project you can walk through from requirement to release.

The seven-day plan covers fundamentals, stories, the screening pitch and a full out-loud rehearsal.

The Allstate Insurance interview process

Candidates report two stages, a screening call followed by interviews with leads, and say the process can vary with the team or location.

Screening Call

Candidates describe this stage as an HR call about your background and your motivations. The work is preparing a clear account of what you have built and why this role at this company appeals to you. Because the role is described as application work for customers and internal processes, anchor your pitch in a project where you shipped something people used, in the language you are strongest in.

What to demonstrate

How clearly you summarise your background and the projects you owned
The reasons you give for wanting this role and this company
Show how your experience matches the skills listed for the role, such as Java, C++ or Python

How to prepare

Write a short chronological pitch of your experience ending on why you want to build software for an insurer, using one concrete project as the example.
List the languages and tools you have used in production and be ready to say which one you would pick for a coding exercise.
Prepare questions for the recruiter: how the lead interviews are structured for this team, which language they expect, and whether coding is live.
Have your location, availability and compensation expectations settled before the call so the conversation stays on your fit.

Interviews with Leads

Candidates report interviews with technical leads or hiring managers that include both behavioral and technical questions. The reported question types span language and OOP fundamentals, problem-solving scenarios (a slow application, a user-facing bug), classic coding (linked list, binary search), high-level system and database design, and behavioral prompts about pressure and prioritising. Prepare each type separately, then rehearse switching between them, since one conversation can move from a design sketch to a story about a deadline.

What to demonstrate

Programming fundamentals: how you compare languages and apply object-oriented design to real code
Problem-solving method when handed a performance or bug scenario
Working code and clear narration on small algorithm questions
Behavioral answers about pressure, priorities and collaboration with other teams

How to prepare

Build a comparison of C++ and Java covering memory management, compilation model, inheritance and generics versus templates, and practise stating when each difference matters.
Write iterative linked-list reversal and iterative binary search in your chosen language from memory, then trace them on empty, one-element and two-element inputs.
Rehearse a fixed sequence for the slow-application and bug-diagnosis scenarios: reproduce, measure, isolate, change one thing, verify.
Sketch a transaction-processing design and a high-availability database layout, with numbers for load, then prepare STAR stories for pressure, prioritisation and a project challenge.

Where candidates lose points

Listing C++ and Java differences as a flat list of features without saying why they matter, or defining OOP by reciting the four pillars with no project behind them

Group the language comparison by consequence: memory management (manual and RAII versus garbage collection), execution model (native versus JVM), inheritance and generics versus templates. For OOP, tie each pillar to a class you wrote, such as polymorphism replacing a type-switch, and mention composition over inheritance.

Answering the slow-application or bug scenarios with guesses such as add a cache or restart the service before measuring anything

Narrate a diagnostic sequence: reproduce the problem, define the symptom in numbers, profile or read the query plan, isolate the biggest cost, change one thing, and re-measure. For a bug, collect the failing input, logs and recent changes, find the smallest reproduction, fix it, and add a regression test.

Submitting a linked-list reversal that loses the next node or fails on an empty list, or a binary search with an off-by-one or an overflowing midpoint

Reverse with explicit previous, current and next references and trace zero, one and two nodes before saying done. For binary search, state your loop invariant, compute the midpoint as low plus half the gap, and say what happens when the target is missing or duplicated.

Answering the millions-of-transactions and high-availability database questions by naming technologies before fixing any numbers or failure assumptions

Convert the load first: one million a day averages roughly twelve per second, so state a peak multiplier. Then cover idempotent writes, a queue for spikes, replication with a stated failover plan, backups and what you would accept losing during failover.

Telling behavioral stories in the first person plural, with no decision of yours and no outcome, then repeating the same story for every prompt

Prepare four STAR stories (challenge, pressure, prioritising, cross-team collaboration) where the action is what you personally decided. End each with an observed result and one change you made afterwards, and note which prompts each story can serve.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

Use the iterative three-pointer reversal: prev = null, curr = head; while curr is not null, save next = curr.next, set curr.next = prev, then advance prev = curr and curr = next. Return prev as the new head.
State the cost: O(n) time and O(1) extra space, because each node's pointer is rewritten exactly once in place.
Walk the edge cases out loud: an empty list returns null, a single node returns itself, and the old head ends up pointing at null because prev starts as null, so no cycle is left behind.
Mention the recursive version (reverse the rest, then head.next.next = head and head.next = null) and why you would not ship it for long lists: it uses O(n) call-stack space and can overflow the stack.
Test it by building lists of length 0, 1, 2 and 5, reversing, and walking the result. For a doubly linked list, swap each node's prev and next pointers instead.

Follow-up

Reverse only the nodes between positions m and n in one pass.
Reverse the list in groups of k nodes, leaving a short final group as it is.
Check whether a linked list is a palindrome using O(1) extra space.

Explain how you would approach a binary search algorithm.

Medium · Coding / Algorithms

Approach

State the precondition: the input is sorted, or more generally there is a monotonic predicate (false...false, true...true). Each step halves the search range, giving O(log n) time and O(1) space when written iteratively.
Write the closed-interval version: lo = 0, hi = n - 1, loop while lo <= hi, mid = lo + (hi - lo) / 2 to avoid integer overflow in Java and C++, move lo = mid + 1 or hi = mid - 1, and return -1 when the loop ends.
For duplicates or insertion points, use the half-open lower-bound version: hi = n, loop while lo < hi, if a[mid] < target then lo = mid + 1 else hi = mid. The loop ends at the first index whose value is not less than target.
Know the library versions: C++ std::lower_bound and std::upper_bound, Python bisect, and Java Arrays.binarySearch, which returns -(insertion point) - 1 when the key is absent and does not guarantee which duplicate it finds.
Check edge cases: empty array, one element, target below the minimum or above the maximum, all elements equal, and infinite loops caused by setting lo = mid while mid rounds down.

Follow-up

Find the first and last positions of a target in a sorted array with duplicates.
Search for a value in a rotated sorted array.
Use binary search on the answer, for example the minimum ship capacity that delivers all packages within D days.

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach

Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.

Follow-up

The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Explain the key differences between C++ and Java.

Medium · Technical / Domain Questions

Approach

Start with the execution model: C++ compiles ahead of time to native machine code for one target platform, while Java compiles to bytecode that the JVM interprets and JIT-compiles when code runs hot. That is the portability-versus-warm-up trade-off.
Memory: C++ gives deterministic lifetimes through RAII, destructors and smart pointers (unique_ptr, shared_ptr), and leaks or dangling pointers are your problem. Java uses a garbage collector and has no destructors (finalize is deprecated), so scarce resources are released with try-with-resources.
Language model: C++ has raw pointers and pointer arithmetic, multiple class inheritance, operator overloading and templates that are instantiated per type. Java has references only, single class inheritance plus multiple interfaces, no user-defined operator overloading, and generics implemented by type erasure, so there is no List<int> and primitives get boxed.
Safety and semantics: an out-of-bounds access in C++ is undefined behaviour, while Java throws ArrayIndexOutOfBoundsException. Java always passes by value (object references are copied). C++ can pass by value, by reference or by pointer.
Finish with when you would pick each one: C++ for latency-sensitive, embedded or memory-constrained code, Java for large service codebases that rely on its runtime checks, tooling and libraries. Tie this to the language you actually use day to day.

Follow-up

Why can a Java generic not be List<int>, and what does boxing cost in a hot loop?
How does RAII in C++ compare with try-with-resources in Java for closing a file or a database connection?
Which methods are virtual by default in Java, and how does that differ from C++?

What is object-oriented programming, and how do you apply it in your projects?

Medium · Technical / Domain Questions

Approach

Define the four ideas in one line each and give a concrete example of each: encapsulation (state is changed only through methods that keep invariants), abstraction (callers depend on an interface, not on internals), inheritance (reuse an is-a relationship) and polymorphism (one call dispatched to different implementations at runtime).
Show how you applied it with one real class design, using an insurance-flavoured example if you have none of your own: a Coverage or Policy interface with auto and home implementations that each compute a premium, so a new line of business is added without editing the callers (open/closed).
Explain why you prefer composition over inheritance in most cases: deep hierarchies create fragile base classes, and composing small objects behind interfaces keeps changes local.
Connect OOP to testability: depending on interfaces and injecting dependencies through the constructor lets you swap a real service for a fake in unit tests.
Name the anti-patterns you avoid: classes that are only getters and setters, god objects, and subclasses that break the parent's contract (the Liskov substitution principle).

Follow-up

Give an example that violates Liskov substitution, such as Square extending Rectangle, and say how you would fix it.
When did inheritance cause you a problem in a real codebase, and what did you replace it with?
How is a virtual method call dispatched at runtime in Java or C++?

Can you describe a challenge you faced in a project and how you overcame it?

Medium · Technical / Domain Questions

Approach

Choose a technical challenge you personally drove, with real stakes: a failing release, a performance problem, a hard integration or a migration. Avoid stories where you only watched someone else solve it.
Structure it as STAR, but keep the situation and task short and spend most of the answer on actions: what you investigated, the options you considered, and why you chose one over the others.
Use "I" for your own decisions and be precise about what the team did, so the listener can tell what you contributed.
Quantify the result (latency cut from X to Y, defects avoided, deadline met) and say how you verified it, not only that it shipped.
Close with what you would do differently. A story that sinks the answer is one that blames a teammate or ends with no measurable outcome.

Follow-up

Which alternative did you reject, and what would have happened if you had chosen it?
How did you confirm your fix solved the problem rather than hiding it?
What did you change in your process afterwards so the same problem would not recur?

How would you optimize a slow-running application?

Medium · Problem-Solving / Case Studies

Approach

Define slow first: which operation, for which users, and whether it is latency (p50 versus p95/p99) or throughput. Reproduce it and record a baseline before changing anything.
Measure where the time goes: use APM traces or request logs to split time across application code, database, network and external calls, use a CPU profiler or flame graph for hot code, and check for saturation (CPU, memory and GC pauses, disk I/O, thread or connection pools).
Fix the measured bottleneck. For the database, run EXPLAIN, add a missing index, remove N+1 query patterns and fetch only the needed columns and rows. For code, replace a quadratic loop with a hash lookup. For I/O, batch calls, pool connections or move work to an async queue.
Add caching only where reads repeat and staleness is acceptable, and state the invalidation rule and memory cost.
Change one thing at a time, re-measure under the same load, and add a load test or benchmark so the regression is caught next time.

Follow-up

The p50 is fine but the p99 is very slow. What would you check?
How would you detect an N+1 query problem in a running service?
When is adding a cache the wrong fix?

Given a scenario where a user encounters a bug, how would you diagnose and resolve the issue?

Medium · Problem-Solving / Case Studies

Approach

Gather the facts before touching code: exact steps, expected versus actual behaviour, error text or screenshot, app or browser version, account and timestamp. Then check scope: one user, a segment, or everyone.
Decide whether to mitigate first. If many users are affected and a deploy or config change lines up with the start time, roll back or switch off the feature flag, then investigate.
Reproduce it in a lower environment with matching version and data, and follow the failing request through logs and traces by request ID at that timestamp. Compare a working input with a failing one to isolate the layer: client, API, or data.
Narrow the cause with git bisect across recent commits or by removing variables one at a time. Then write a failing test that reproduces the bug before fixing the root cause, not the symptom.
Ship the fix through review, confirm it with the user and with monitoring, and record why existing tests missed it, adding the test or alert that would have caught it.

Follow-up

What do you do when you cannot reproduce the bug locally?
How do you decide between a hotfix and a rollback?
The bug only appears for users in one time zone. What would you suspect first?

How would you design a system to handle millions of transactions per day?

Medium · System Design / Architecture

Approach

Size it first: there are 86,400 seconds in a day, so 1 million transactions per day averages about 12 per second and 10 million about 116 per second. Assume a peak several times the average and say so. At that rate the hard part is correctness (no lost or duplicated transactions), not raw throughput.
Use premium payments as the worked example. Put stateless API servers behind a load balancer, with an idempotency key on every payment request stored under a unique constraint so a client retry cannot charge a policyholder twice. Write to an ACID relational database as an append-only ledger of payments, with the policy account balance updated in the same transaction.
Downstream work (receipts, notifications, analytics, reconciliation) goes through a message queue. Use a transactional outbox so the database commit and the event cannot diverge. Consumers get at-least-once delivery, so they must be idempotent, with retries using exponential backoff and a dead-letter queue.
Scale reads with replicas and caching of reference data. Partition by account or policy ID only when one primary can no longer handle the writes, and name the cost: transactions across shards and global reports become harder. Contrast claims: a claim is long-running and changes state (submitted, under review, approved, paid), so model it as a state machine with an event history and idempotent transitions rather than as a single payment-style write.
Operate it: monitor error rate, p99 latency and queue lag, and run a reconciliation job that compares the ledger with downstream systems.

Follow-up

The client times out and retries after the server already committed. How do you prevent a double charge?
What happens if the database commit succeeds but publishing the event fails?
If you shard by account ID, which queries become expensive and how do you serve them?

Discuss the considerations you would take into account for database design in a high-availability system.

Medium · System Design / Architecture

Approach

Turn high availability into numbers: an availability target (99.9% allows about 8.8 hours of downtime a year, 99.99% about 53 minutes), a recovery point objective for how much data you can lose, and a recovery time objective for how fast you must be back.
Replication: a synchronous standby gives zero data loss on failover but adds latency to every commit, while asynchronous replicas are faster but can lose the last transactions and serve stale reads. Place replicas in separate availability zones.
Failover: automate promotion with health checks and fencing so the old primary cannot keep accepting writes (split brain), or use a consensus-based database (Raft or Paxos) that needs a quorum before committing. Clients need connection retry and idempotent writes to get through the switch.
Replication is not a backup, because a bad DELETE replicates too. Keep base backups plus write-ahead log archiving for point-in-time recovery, and test restores regularly.
Design the schema for zero-downtime changes: expand and contract migrations (add a nullable column, backfill in batches, switch reads, drop later), avoid long table locks, and use connection pooling. Route read-your-writes queries to the primary.

Follow-up

How do you prevent split brain when the primary is only partly unreachable?
How would you add a non-null column to a very large table without downtime?
When would you accept asynchronous replication and its possible data loss?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Seven days built around the two reported rounds: day one for the screening pitch, days two to five for the language, coding, debugging and design questions candidates report, day six for stories, and day seven for a full rehearsal.

Day 1 — Map the loop and write the screening pitch

Write out the two reported stages and mark which question types you expect in the lead interviews: language and OOP, problem-solving, coding, design, behavioral.
Write a short pitch of your background and why you want to build software for an insurer, using one project you shipped as the example.
List the questions you will ask the recruiter about the lead interviews, the expected language and whether coding is live.

Deliverable: A one-page loop map, a written screening pitch, and a list of recruiter questions.

Day 2 — Language comparison and object-oriented design

Build a C++ versus Java table covering memory management, compilation model, multiple inheritance, templates versus generics, and how errors surface (undefined behavior versus exceptions).
Sketch a small class hierarchy in your strongest language: a Coverage or Policy interface with two implementations, using encapsulation and polymorphism, and note where composition beats inheritance.
Map each SOLID principle to one concrete code smell it fixes. Then rehearse aloud a 90-second answer to C++ versus Java grouped by memory, execution model and type system, and a 2-minute OOP answer built around your class sketch.

Deliverable: The comparison table, one class sketch with SOLID notes, and two recorded or spoken answers (90 seconds and 2 minutes).

Day 3 — Linked list and binary search from memory

Write iterative linked-list reversal with previous, current and next references, then the recursive version, and state time and space for each.
Write iterative binary search, then variants for first and last occurrence, and explain your invariant and midpoint calculation.
Do one harder merge-style problem from the practice set while narrating each decision as if pairing with the interviewer.

Deliverable: Working code for four functions in one language, each traced on empty, one-element and multi-element input.

Day 4 — Slow applications and user-reported bugs

Write a diagnosis checklist for a slow application: reproduce, measure, profile, check database query plans, look for repeated queries, change one thing, re-measure.
Write a bug-diagnosis checklist: collect the failing input and logs, check recent changes, find the smallest reproduction, fix, add a regression test, and report back to the user.
Work the practice items on keyset paging and a stalling table, writing down the evidence each one gives you.

Deliverable: Two checklists and a written walk-through of the two practice items, each ending in what you would measure first.

Day 5 — Transaction and high-availability design

Size a transaction system: convert millions per day to an average per second, pick and state a peak multiplier, and list the components: stateless API, idempotency keys, queue, database.
Write the high-availability database layout: primary and replicas, synchronous versus asynchronous replication, failover, backups, connection pooling and online schema changes.
For each design, list what fails first at ten times the load and what you would accept losing during a failover.

Deliverable: Two one-page design sketches with numbers and a failure table.

Day 6 — STAR stories for the behavioral questions

Write four STAR outlines: a project challenge, working under pressure, prioritising across deadlines, and collaborating with product, QA or customer service.
Rewrite each so the action section names your own decisions and the result is something you observed.
Practise the practice-set item on telling other teams that a change breaks them as an extra cross-team story, then say all five aloud.

Deliverable: Four STAR outlines plus one cross-team story, each marked with the prompts it can answer.

Day 7 — Full rehearsal of the lead interviews

Say your screening pitch, then answer in sequence: C++ versus Java, a linked-list reversal, a slow-application scenario, a transaction design, then the pressure and prioritising questions.
Mark every place you hesitated, lost an edge case or fell back on a vague phrase, and rework those answers.
Pick one project and prepare a walk-through from requirement to release, including testing, code review and what you would change.

Deliverable: A marked-up rehearsal with the weakest three answers rewritten and one project walk-through.

STAR stories for the Allstate Insurance loop

Candidates report that the lead interviews mix behavioral questions with technical ones, and the reported prompts are about working under pressure, prioritising several deadlines and overcoming a project challenge. Reports also mention collaboration with cross-functional teams and explaining technical ideas to non-technical people, and list teamwork, integrity and customer focus as themes to illustrate. Prepare STAR stories where your own decisions are clear.

Can you describe a challenge you faced in a project and how you overcame it?
Share an experience where you had to collaborate with a team to achieve a common goal.
How do you approach explaining technical concepts to someone without a technical background?

Describe a time when you had to work under pressure. How did you handle it?

Medium · Behavioral / Leadership Questions

Approach

Pick a situation where the pressure was concrete and external: a fixed release date, a production defect, or a dependency that arrived late. Name it in a sentence or two, then move straight to what you decided. A story where things were simply busy gives the listener nothing to evaluate.
Show triage: what had to ship, what could be cut or deferred, and who agreed. Say how you protected quality under the squeeze, for example keeping the tests and the rollback path while trimming scope, rather than skipping verification.
Describe how you communicated while it was happening: telling the product manager or QA early what would slip, and posting short status updates, instead of surfacing bad news at the deadline.
Close with the result you actually observed (shipped on the date, shipped a reduced scope, caught a defect before release) and one thing you changed afterwards, such as an earlier estimate check or a smaller release.
Avoid the two failing versions: a hero story built on long hours with no decision in it, and a story where you were calm because someone else handled the hard part.

Follow-up

What would you have done if the deadline could not move and the scope could not shrink?
What did you cut, and how did you decide that was safe to cut?
Looking back, at what point could you have raised the risk earlier?

How do you prioritize tasks when faced with multiple deadlines?

Medium · Behavioral / Leadership Questions

Approach

Open with a method, then prove it with a story. A workable method: list every commitment with its deadline, who is waiting on it, the cost of missing it and the effort left, then order by cost of delay and dependencies rather than by who is loudest.
Clarify ownership before you rank. When two requests both claim top priority, ask the requesters or your lead which one matters more to the business and say that decision is theirs to make, with you supplying the trade-offs and estimates.
Give a real example with two or more deadlines in conflict. State what you did first, what you deferred, whom you told, and how you renegotiated scope or dates early rather than quietly missing one.
Mention the tools you used to stay honest, such as a ticket board or a written daily list, and how you handle interruptions like a production bug that outranks planned work.
Avoid answers that claim you simply work harder, treat everything as urgent, or never miss a date. State a trade-off you accepted and its result.

Follow-up

Two managers each say their task is the top priority. What do you do?
A deadline you committed to is going to slip halfway through. Whom do you tell and what do you bring with you?
How do you handle an urgent production bug arriving while you are mid-feature?

Tell callers you do not own that their integration breaks

Medium · deprecation · compatibility · stakeholders

A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.

Approach

Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.

Follow-up

How would you detect a consumer that reads the field only during a monthly export?
One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?