Interview guides · AlEn

AlEn Software Engineer Interview Guide

AlEn Software Engineer interview guide: reported rounds, coding, SQL, REST and behavioral questions with answer approaches and a 7-day prep plan.

The 60-second brief

Candidate reports describe AlEn as a company that ships software for users, with the Software Engineer role touching products that range from mobile applications to large enterprise systems. Reported day-to-day work includes building new features, tuning existing code, taking part in code reviews and architecture discussions, and helping with deployments, alongside product managers and designers. The reports are general and do not name a product line, team or tech stack beyond examples such as Java, Python and JavaScript, Git, Agile and Scrum, with AWS, Azure, React, Angular and DevOps tooling listed as nice-to-have. Read the job posting for the team you are applying to and let it narrow the language and framework you rehearse in.

This guide is for candidates interviewing for a Software Engineer position at AlEn. It covers the three reported stages, how to answer the reported technical, problem-solving, coding and behavioral questions, and a seven-day plan. The reported question list is a pattern sample drawn from a small number of reports, so use it to build skills rather than to memorise answers.

Candidates report three stages over roughly three to five weeks: Initial Screening, Technical Interviews and Behavioral Discussions. Reported questions are grouped by type (technical, problem-solving, behavioral, coding) and are not tied to a particular stage.

Focus areas

Software Engineering (General)
Problem Solving
Programming Fundamentals

Overview

The reported question set is broad rather than deep. It mixes language-level concepts (object-oriented versus functional programming, how hash tables work), classic coding exercises (reverse a linked list, binary search, find two numbers that add up to a target), practical engineering topics (slow SQL queries, REST API design, debugging a failing application, refactoring legacy code, release stability) and a large block of behavioral prompts. Fundamentals preparation therefore pays off more than studying any single advanced topic.

Roughly two thirds of the reported prompts are conversational. Several of them sound technical but are answered best as a short story with a decision in it: identifying a critical bug in production, experience with Git, trading quality against speed, and gathering requirements for a feature. The skill to rehearse is moving between a clean explanation of a concept and a concrete example from your own work, ideally in the same answer.

Behavioral Discussions are reported as a stage of their own, built around your previous experiences and how you collaborate. Reported prompts cover conflict with a teammate, leading an initiative, prioritising under tight deadlines, constructive feedback and what drives your standard of work. Build a bank of four or five real projects and map each to several of these prompts, so that you are choosing between stories instead of inventing one on the spot.

Because reports differ by team, spend your first session reading the job posting. Note which language it names, whether it mentions cloud services or a front-end framework, and which of the reported question types (SQL, REST, debugging) match the day-to-day work it describes. Then use the plan below, which pairs timed coding practice with spoken answers to the process and behavioral questions.

The AlEn interview process

Candidates report three stages over about 3-5 weeks: Initial Screening, Technical Interviews and Behavioral Discussions. Reported questions are grouped by type, not assigned to a stage, so this guide only says which question types suit each stage.

Initial Screening

Candidates describe the first stage as a review of your background and your fit for the role. Little else is reported about its format, so treat it as a conversation about your CV and the posting. Expect to explain your recent work, your main language and tooling, and why you want this role. Use it to confirm what the remaining stages will contain and who you will speak with, so the later preparation is aimed correctly.

What to demonstrate

How your past projects and languages map onto a Software Engineer role
Whether you can describe your own contribution clearly and briefly
Practical fit with the role, as the stage name suggests

How to prepare

Write a short walk through your last two or three projects: the problem, your part, the stack (for example Java, Python or JavaScript), and one result you can state precisely.
Prepare a two-sentence answer on experience with Git and Agile or Scrum, since both are listed among the skills candidates describe.
List questions to ask the recruiter: which stages follow, what the technical interviews cover, and which team the role sits in.

Technical Interviews

The second stage is reported as technical interviews that test technical skill and problem-solving. The reported questions that fit this format cover fundamentals (object-oriented versus functional programming, hash tables, REST API design), SQL tuning, debugging and refactoring, and coding exercises such as reversing a linked list, binary search and the two-number target problem. Practise writing working code while explaining your choices, and practise the process questions as structured spoken answers with concrete steps.

What to demonstrate

Working code and sound reasoning on data structures and algorithms
Command of language and engineering fundamentals such as OOP versus functional style, REST and SQL
A structured approach to debugging, refactoring and release quality

How to prepare

Write reverse a linked list, binary search and the two-number target problem from memory in your interview language, then state each solution's time and space complexity aloud.
Prepare a debugging sequence (reproduce, narrow, inspect logs and recent changes, fix, add a regression test) and a slow-query sequence (read the plan, check indexes, reduce rows early, test again).
Outline REST design in five points you can recite: resources and URLs, HTTP methods and status codes, idempotency, pagination and filtering, versioning and errors.

Behavioral Discussions

Candidates describe the last stage as conversations about your previous experiences, used to judge collaboration and fit with a team. The reported behavioral prompts are conflict with a teammate, leading a project, prioritising under deadlines, constructive feedback and motivation for quality work. Answer each with a specific situation, the decision you made, and a result you can state. Prepare a few stories that can flex across several prompts, and remember that a story told only in the plural hides what you did.

What to demonstrate

Collaboration and communication, as the stage description reports
How you describe decisions, trade-offs and results from earlier work
Adaptability and how you respond to feedback or disagreement

How to prepare

Build a story bank of four or five projects. Next to each, note the bug, trade-off, conflict, leadership moment and feedback it can illustrate.
Rehearse each story aloud in a clear order: situation, task, your actions, result, what you would change. Cut any detail that does not support the decision.
Prepare one answer on adapting your working style to a team and one on keeping a team aligned on goals, since both appear among reported scenarios.

Where candidates lose points

Solving the linked-list, binary-search or two-number problem silently, then stating complexity only when asked

Explain the idea first, write the code, trace a small example, then state time and space complexity yourself. For binary search, cover the empty array, one element, a missing target and the loop bounds.

Answering the object-oriented versus functional question with definitions and no trade-offs

Contrast state and mutation with pure functions and immutability, then give one case where each style fits and one real example from code you have written.

Describing slow SQL as add an index with no diagnosis

Start from the query plan, find the scan or join that costs most, check predicates and indexes, reduce rows early, and re-measure. Mention when an index will not help.

Telling the production bug, conflict or leadership story with no clear personal action or result

For each story write down what you decided, what you did, and one number or outcome. Cut background until the decision is the centre of the answer.

Giving a generic debugging or release-stability answer that would fit any system

Use a concrete order: reproduce, check recent changes and logs, bisect, fix, add a test. For releases, name tests, staged rollout, monitoring and a rollback plan.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

Clarify the node shape (singly linked, value plus next) and whether to reverse in place, then write the iterative three-pointer version: prev = None, curr = head; while curr: nxt = curr.next; curr.next = prev; prev = curr; curr = nxt; return prev.
State the complexity: O(n) time, O(1) extra space, every node visited once and each next pointer rewritten once.
Walk the edge cases aloud: empty list returns None, a single node returns itself, and in a two-node list the old head's next becomes None; the common bug is returning head or curr instead of prev.
Offer the recursive version: reverse the rest, then head.next.next = head; head.next = None; it is O(n) time but O(n) call-stack space, and in Python it fails on long lists because the default recursion limit is about 1000.
Test it by building a list from an array, reversing, converting back and comparing; 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.
Reverse the list in groups of k nodes.
How would you detect a cycle before reversing, and what happens if you reverse a list that has one?

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

State the precondition: the array is sorted (or, more generally, a predicate is monotonic over the range). Keep a closed interval [lo, hi]: while lo <= hi, mid = lo + (hi - lo) // 2; return mid on a match, else set lo = mid + 1 or hi = mid - 1; return -1 when the loop ends.
Explain the details that cause bugs: (lo + hi) / 2 can overflow a Java int, so use lo + (hi - lo) / 2 or (lo + hi) >>> 1 (Python integers do not overflow); in a half-open loop (while lo < hi) setting lo = mid instead of mid + 1 can loop forever.
Complexity: O(log n) time; O(1) space iteratively, O(log n) stack space if written recursively.
Cover the variants: lower bound (first index with a[i] >= target) using [lo, hi) and hi = mid; first and last occurrence with duplicates; Python bisect_left and bisect_right; Java Arrays.binarySearch returns -(insertion point) - 1 when the key is absent and does not say which duplicate it finds.
Test empty input, one element, a target below the minimum and above the maximum, and runs of duplicates; mention binary search on the answer (integer square root, minimum capacity) where the search space is values, not indices.

Follow-up

Search for a target in a rotated sorted array.
Return the first and last positions of a target that appears many times.
How would you search a sorted source whose length you do not know?

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

Medium · Coding / Algorithms

Approach

Clarify first: return indices or values, whether exactly one answer is guaranteed, whether the same element may be used twice, whether duplicates and negative numbers occur, and whether the array is sorted.
Brute force: check every pair with two nested loops, O(n^2) time and O(1) space, as the baseline to improve on.
Hash map in one pass: for i, x in enumerate(nums): if target - x in seen: return [seen[target - x], i]; then seen[x] = i. O(n) average time and O(n) space; checking before inserting stops an element pairing with itself and still handles [3, 3] with target 6.
Sorted alternative: two pointers from both ends, moving left up when the sum is too small and right down when too large, O(n) on sorted input with O(1) extra space; if you must sort first it is O(n log n) and you have to keep original indices as (value, index) pairs.
Edge cases: no valid pair (return an empty result or raise, as agreed), and in Java target - x or x + y can overflow int at the extremes, so compute in long.

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?
The input is a stream too large to hold in memory. What changes?

Explain how hash tables work and their typical use cases.

Medium · Coding / Algorithms

Approach

Mechanism: a hash function turns the key into an integer, which is reduced (modulo or bit mask) to a bucket index in an array, giving average O(1) insert, lookup and delete as long as the hash spreads keys well and the load factor stays bounded.
Collisions: separate chaining keeps a list per bucket (Java HashMap turns a bucket into a red-black tree once its chain passes 8 entries and the table has at least 64 buckets); open addressing probes other slots (linear or quadratic probing, double hashing; CPython's dict uses open addressing) and needs tombstones for deletion.
Resizing and worst case: when the load factor passes a threshold (0.75 by default in Java HashMap) the table doubles and rehashes, an O(n) step that is amortised O(1) per insert; if all keys collide, operations degrade to O(n), which is why Python randomises string hashes against hash flooding.
Key contract: equal keys must have equal hashes (equals and hashCode in Java, eq and hash in Python), and a key must not change after insertion; that is why Python lists are unhashable while tuples are.
Use cases and limits: membership and deduplication (sets), frequency counting, caching and memoisation, indexing records by id, hash joins in databases, complement lookups as in two-sum; a hash table gives no sorted order, so use a balanced tree (Java TreeMap) for range queries or ordered iteration.

Follow-up

Design an LRU cache with O(1) get and put.
What happens if you mutate an object after using it as a key in a HashMap?
When would you choose a balanced binary search tree over a hash table?

Describe the time complexity of your solution to a common problem.

Medium · Coding / Algorithms

Approach

Pick a problem you can derive end to end, such as two-sum with a hash map or merge sort, and say what n counts (elements, nodes, characters) before giving any bound.
Show the derivation: nested loops multiply, sequential phases add and the larger term dominates, and recursion becomes a recurrence; merge sort is T(n) = 2T(n/2) + O(n) = O(n log n), binary search is T(n) = T(n/2) + O(1) = O(log n).
Distinguish worst, average and amortised cases: quicksort is O(n log n) on average but O(n^2) in the worst case, hash lookups are O(1) on average and O(n) worst case, and appending to a dynamic array (Python list, Java ArrayList) is amortised O(1) because capacity grows geometrically.
Always add space complexity, including the recursion stack and any copies, and name hidden costs: x in a Python list is O(n) while in a set it is O(1) on average, slicing copies, and building a string by repeated concatenation in a loop can be O(n^2).
Tie the bound to input size: with n around 10^5, an O(n^2) solution is about 10^10 basic steps and too slow, while O(n log n) is a few million; state constant factors and measure when two options share the same bound.

Follow-up

Can this be done faster, and what lower bound applies to comparison-based sorting?
With two inputs of sizes n and m, when is the cost O(n + m) and when O(n * m)?
Why is appending to a dynamic array amortised O(1) even though some appends copy the whole array?

How would you optimize a slow SQL query?

Medium · Technical / Domain Questions

Approach

Measure before changing anything: run EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN (MySQL) on production-like data, find the node with the largest actual time, and compare estimated to actual row counts; a large gap points to stale statistics, so run ANALYZE first.
Fix access paths: a sequential scan on a large table with a selective filter needs an index; in a composite index put equality columns before the range column, e.g. (status, created_at) for WHERE status = ? AND created_at > ?, and add covered columns so the plan can become an index-only scan.
Make predicates sargable: WHERE DATE(created_at) = ?, LOWER(email) = ?, a leading-wildcard LIKE '%x' or an implicit type cast all stop a plain index being used; rewrite as a range (created_at >= ? AND created_at < ?) or add an expression index.
Cut the work the query does: select only needed columns instead of SELECT *, filter before joining, replace a correlated subquery with a join or EXISTS, avoid a join that fans out and is then cleaned up with DISTINCT, and use keyset pagination instead of a large OFFSET.
State the trade-off and verify: every index slows inserts and updates and costs storage, so re-read the plan after the change and time it again; if the query is inherently heavy, consider a materialized view, a summary table or caching.

Follow-up

The index exists but the planner still chooses a sequential scan. When is that the right choice?
The query is fast when run alone but slow under production load. What would you check?
How would you choose the column order of a composite index for a query that filters on one column and sorts on another?

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?

Explain the difference between object-oriented programming and functional programming.

Medium · Technical / Domain Questions

Approach

Define both precisely: OOP bundles state with the methods that change it and relies on encapsulation, inheritance and polymorphism through dynamic dispatch; FP builds programs from pure functions over immutable values, with functions as first-class values (higher-order functions, map/filter/reduce, composition).
Name the core trade-off (the expression problem): in OOP adding a new type is one new class, but adding a new operation touches every class; with algebraic data types and pattern matching in FP, adding an operation is one function, but adding a variant touches every function.
Give the practical consequences: pure functions are easier to unit test and safe to run in parallel because there is no shared mutable state; immutability costs extra allocation, which persistent data structures reduce through structural sharing.
Ground it in Java or Python: the same discount rule written as a Strategy interface with subclasses versus a function passed as an argument; Java streams and lambdas, Python comprehensions, functools and frozen dataclasses show both styles in one language.
Close with how I mix them: a functional core of pure business logic with side effects (database, network, logging) pushed to a thin imperative shell, and composition preferred over deep inheritance.

Follow-up

How would you handle side effects such as database writes or logging in a functional style?
When does inheritance cause problems, and what would you use instead?
Is a Java stream pipeline whose lambda appends to an external list still functional? What can go wrong if it runs in parallel?

Discuss the principles of RESTful API design.

Medium · Technical / Domain Questions

Approach

Model resources as nouns with stable URIs (/orders/{id}/items) and let the HTTP method carry the verb: GET is safe, PUT and DELETE are idempotent, POST is neither, and PATCH is not guaranteed to be idempotent.
Keep the server stateless: each request carries its own authentication (for example a bearer token), so any instance can serve it; that enables horizontal scaling and HTTP caching with Cache-Control and ETag plus If-None-Match returning 304 Not Modified.
Use status codes precisely and one consistent error body: 201 Created with a Location header, 204 No Content, 400 for malformed input, 401 unauthenticated versus 403 forbidden, 404, 409 Conflict, 429 with Retry-After, and 5xx only for server faults.
Design collections and change: pagination (cursor-based for large or changing data, offset for simple cases), filtering and sorting through query parameters, and versioning in the path (/v1) or a header, with only additive changes inside a version.
Make retries safe: accept an Idempotency-Key header on POST requests that create orders or payments so a retry after a timeout returns the original result, and use ETag with If-Match for optimistic concurrency, returning 412 Precondition Failed on a stale write.

Follow-up

How would you model an action that is not CRUD, such as cancelling an order?
Why can offset pagination return duplicate or skipped rows while new rows are being inserted, and how does a cursor avoid it?
When would you choose gRPC or GraphQL over REST?

Describe your process for gathering requirements for a new feature.

Medium · Problem-Solving / Case Studies

Approach

Start from the problem, not the solution: who the user is, what they are trying to do, what they do today without the feature, and which metric would show the feature worked.
Separate functional requirements, written as user stories with testable acceptance criteria (Given/When/Then), from non-functional ones: latency, expected load, availability, security and privacy, accessibility and supported platforms.
Surface edge cases and constraints early: permissions and roles, empty and error states, data migration, backward compatibility with existing clients and APIs, and dependencies on other teams or services.
Scope explicitly: prioritise with MoSCoW or a similar method, define the smallest releasable version and an out-of-scope list, write a short spec and confirm it with the product manager and designer, using a mock-up or prototype where the flow is unclear.
Treat requirements as living: revisit them each iteration, demo early to the people who asked for the feature, and record decisions and changed assumptions so the team works from one version.

Follow-up

Two stakeholders want conflicting things. How do you settle priority?
A requirement turns out to be ambiguous halfway through a sprint. What do you do?
How do you estimate work when the requirements are still incomplete?

What steps would you take to ensure a software release is stable and robust?

Medium · Problem-Solving / Case Studies

Approach

Gate the merge: unit, integration and a small set of end-to-end smoke tests in CI, code review, static analysis and linting, with a green pipeline required before merge; then test on a staging environment that mirrors production configuration and realistic data volumes.
Decouple deploy from release: ship behind feature flags and roll out gradually with a canary or phased rollout (for example 1%, 10%, 50%, 100%) or blue-green switching, comparing error rate, latency and key business metrics against the baseline at each step.
Keep every step reversible: database migrations follow expand-and-contract (add the new column, write to both, backfill, switch reads, remove the old one later) so old and new code run side by side, and API changes stay additive.
Have a tested rollback: one command or a flag flip, automatic halt of the rollout when alert thresholds are crossed, a named on-call owner and release notes listing what changed.
After release: watch dashboards and alerts, add a regression test for every escaped bug, and run a blameless postmortem when an incident happens.

Follow-up

How would you sequence a release that renames or drops a database column?
The canary looks healthy except for one business metric that dips slightly. What do you do?
Flaky tests keep blocking releases. How would you handle them?

Outline your approach to refactoring legacy code.

Medium · Problem-Solving / Case Studies

Approach

Understand and pin behavior first: find the entry points and callers, then write characterization tests that capture what the code currently does (including odd behavior) before changing a line.
Create seams so the code becomes testable: inject dependencies such as the database, clock and network clients, and extract interfaces around them (the techniques in Michael Feathers's Working Effectively with Legacy Code).
Change in small behavior-preserving steps: extract method, rename, remove duplication, one refactoring per commit, refactoring commits kept separate from behavior changes, and CI green after each step.
For larger replacements use the strangler fig pattern: put a facade in front, move traffic to the new implementation piece by piece behind a feature flag, and run old and new in parallel comparing outputs before cutting over; avoid a big-bang rewrite.
Prioritise and justify with evidence: target hot spots with high change frequency and high complexity, and tie the work to a measurable cost such as bug count in that module or lead time for changes there.

Follow-up

The code has no tests, no documentation and the original author has left. Where do you start?
How would you convince a product manager to give you time for this refactoring?
When is a full rewrite justified instead of incremental refactoring?

How would you approach debugging a failing application?

Medium · Problem-Solving / Case Studies

Approach

Define the failure precisely before theorising: what fails (crash, error response, wrong output, timeout), for which users, since when, and at what rate; then reproduce it, and check whether it is limited to one region, app version, input shape or host.
Correlate with changes: recent deploys, configuration or feature-flag changes, dependency upgrades, traffic spikes, expired certificates or credentials; if production is hurting and a recent change lines up, roll it back or flag it off first and find the root cause afterwards.
Read the evidence in order: logs around the first failing request, the stack trace, metrics (error rate, latency, CPU, memory, connection-pool saturation) and distributed traces to locate which service or hop actually fails, since the service throwing errors is often only the symptom.
Narrow it down: reproduce locally with the same input and configuration, use git bisect to find the commit, add targeted logging, and check resource exhaustion such as a full disk, OOM kills, file-descriptor limits or exhausted database connections.
Close the loop: write a test that fails on the bug, fix it, verify in staging, add an alert or dashboard for the signal that would have caught it earlier, and write a short blameless postmortem.

Follow-up

The failure only happens in production and never locally. What differences would you check?
The error appears once every few hundred requests. How would you find it?
How do you decide between rolling back and shipping a hotfix?

Suggested practice checklist

The plan starts with the loop and your story bank, spends the middle days on coding, SQL, REST and process questions, and ends with spoken behavioral practice across the reported prompts.

Day 1 — Map the loop and build a story bank

Write the three reported stages (Initial Screening, Technical Interviews, Behavioral Discussions) and mark the one you are least ready for.
Read the job posting and note the language, any cloud or front-end tools, and anything about SQL or APIs.
List four or five projects and, for each, the bug, trade-off, conflict and feedback it could illustrate.

Deliverable: A one-page loop map, a posting checklist and a project table with the story each project can support.

Day 2 — Core coding exercises

In your main language, write reverse a linked list iteratively and recursively, and say the cost of each.
Write binary search with clean bounds, then test an empty array, one element and a missing target.
Solve the two-number target problem with a brute-force pass and a hash-map pass, and state both complexities.

Deliverable: Three working solutions with complexity notes and a list of the edge cases each one handles.

Day 3 — Fundamentals out loud

Explain how a hash table works: hashing, buckets, collisions, resize, and average versus worst case.
Prepare a comparison of object-oriented and functional programming with one example of each from your own code.
Outline REST design: resources, methods, status codes, idempotency, pagination and versioning.

Deliverable: Three spoken explanations, each ending with a trade-off and a worked example you can give quickly.

Day 4 — SQL tuning and debugging

Take one slow query you know, read its plan, and write the steps you would use to speed it up.
Write your debugging sequence for a failing application and rehearse it on a past incident.

Deliverable: A written tuning checklist, a corrected fan-out query with explanation, and a debugging sequence tested on one real case.

Day 5 — Requirements, releases and refactoring

Write how you gather requirements: whom you ask, what you clarify, how you record scope and acceptance criteria.
Write a release checklist covering tests, staged rollout, monitoring and rollback.
Outline a refactor of a legacy module: add tests first, make small safe steps, and measure behavior unchanged.

Deliverable: Three structured answers, each with a concrete example from your own work.

Day 6 — Technical-flavoured stories

Write the production bug story: detection, containment, root cause, fix and prevention, with real numbers.
Prepare a Git answer with workflow, merge versus rebase and recovery commands, plus one real episode.
Write the quality versus speed story and the method you use to prioritise under deadlines.

Deliverable: Four spoken answers with notes on result, trade-off and what you would change.

Day 7 — People stories and full run

Rehearse the teammate conflict, project leadership and constructive feedback stories aloud, using singular statements about your own decisions.
Prepare your answer on what drives your quality standard, tied to two habits.
Run a full session: two coding problems, one process question and three behavioral prompts, then note the weakest answer and redo it.

Deliverable: Four spoken behavioral answers and one full practice-session review with the weakest answer rewritten.

Stories to prepare for the AlEn behavioral rounds

Candidates describe the last stage as conversations about previous experience, aimed at collaboration and fit. For a Software Engineer, that means stories about shipping and fixing software with other people: incidents, trade-offs, disagreements and feedback. Each answer needs a specific situation, your own decision and a result. Prepare reported prompts first, then the extra scenarios on adapting and alignment.

Discuss a time when you had to adapt your working style to fit a team's needs.

Describe a time when you identified a critical bug in a production environment.

Medium · Technical / Domain Questions

Approach

Choose an incident you personally diagnosed or fixed. State the context briefly: what the system did, how the bug surfaced (alert, user report, dashboard), and why you judged it severe.
Walk through the sequence: confirm the symptom, limit the damage (rollback, feature flag, hotfix), find the root cause from logs, metrics or a bisect, then ship the fix with a regression test.
Quantify the impact with numbers you really had: affected users, error rate, duration of exposure, data that needed repair. Say how you confirmed the fix worked.
Finish with prevention: the alert, test, review checklist or process change you added. Avoid blaming a person; describe the gap in the system that let the bug through.

Follow-up

Why did you choose rollback over fixing forward, or the reverse?
How did you keep stakeholders informed while the incident was open?
What change would have caught this bug before release?

What is your experience with version control systems like Git?

Medium · Technical / Domain Questions

Approach

Answer in three layers: daily workflow (short-lived feature branches, small commits, pull requests, review), collaboration (resolving merge conflicts, branch protection, tagging releases) and recovery (reflog, revert, cherry-pick, stash, bisect).
State a clear position on merge versus rebase: rebase to tidy your own unpublished branch, merge to preserve shared history, and never rewrite history others have pulled.
Know the traps: revert creates a new commit that undoes an earlier one and is safe on shared branches, whereas reset moves a branch pointer and needs care once the commits are pushed. Force-push only with lease and only on your own branch.
Anchor the answer with one real episode, such as finding a regression with git bisect or untangling a bad merge, and say what the team did afterwards to avoid a repeat.

Follow-up

A commit with a bug is already on the shared main branch. How do you undo it?
How would you locate the commit that introduced a regression across a few hundred commits?
When would you squash commits, and when would you keep them separate?

Think of a time when you had to make a trade-off between quality and speed. How did you handle it?

Medium · Problem-Solving / Case Studies

Approach

Pick a situation with a real deadline and a real quality risk. Name what was at stake on each side, such as a launch date versus test coverage or a cleaner design.
Explain how you decided: which parts you protected (data integrity, security, the main user path) and which you consciously cut or simplified, and who you agreed that with.
Describe the safeguards: a feature flag, a limited rollout, monitoring, or a recorded follow-up ticket for the shortcut. Say whether and when it was repaid.
Report the outcome honestly, including anything that went wrong, and say what you learned about when to hold the line.

Follow-up

What would you have refused to cut, however tight the deadline?
How did you make sure the shortcut was repaid later?
Looking back, was the trade-off right?

Describe a situation where you had a conflict with a teammate. How did you resolve it?

Medium · Behavioral / Leadership

Approach

Choose a real disagreement, ideally about a technical decision or how to split work, where both positions were reasonable. Avoid a story that casts the other person as plainly wrong.
Show that you sought their reasoning first: you asked questions, restated their view, and found what you both wanted (a stable release, a maintainable design).
Describe how it was settled: data, a small experiment, a design doc, or escalating the decision to the owner. Stress the mechanism rather than who won.
Close with the result for the project and the relationship, and one thing you now do differently to surface disagreements earlier.

Follow-up

How did the other person feel about the outcome afterwards?
What if your manager had decided against your view?
How would you handle the same conflict with a more senior colleague?

What motivates you to deliver high-quality work?

Medium · Behavioral / Leadership

Approach

Name one or two genuine drivers and keep them concrete, such as not wanting users to hit a bug you could have prevented, or pride in code a teammate can safely change.
Prove each driver with behavior: writing tests before refactoring, reviewing your own diff before asking for review, adding monitoring after a launch, or following up on how a release performed.
Add one episode where this standard had a cost (extra time, a harder conversation) and explain how you balanced it against delivery.
Avoid slogans such as caring about excellence. A claim with no habit behind it sounds rehearsed.

Follow-up

Describe a time your standard for quality clashed with a deadline.
How do you respond when a teammate works to a lower bar than you?
What does a high-quality code review look like to you?

Can you share an experience where you had to lead a project or initiative?

Medium · Behavioral / Leadership

Approach

Pick an initiative where you owned the outcome, even without a title: a migration, a feature, a tooling improvement. State the goal and why it needed an owner.
Explain how you organised the work: breaking it into pieces, assigning or taking tasks, setting checkpoints, and how you communicated progress and risks to the people affected.
Describe one obstacle (a blocked dependency, a missed estimate, a teammate struggling) and the specific action you took in response.
Use singular statements about your own decisions. A story told entirely in the plural leaves your contribution invisible. End with the measurable result and what you would change next time.

Follow-up

How did you handle a teammate who was falling behind?
What did you get wrong in the plan, and how did you adjust?
How did you decide the project was successful?

How do you prioritize tasks when working under tight deadlines?

Medium · Behavioral / Leadership

Approach

Describe a repeatable method before the story: list everything due, mark the must-have items, estimate effort, impact and risk, and note dependencies and blockers.
Say how you negotiate scope, not just effort: you tell the requester what will slip or be reduced, rather than silently working longer hours.
Give one real example in which you cut or deferred items, who agreed to the new scope, and what the result was against the deadline.
Cover interruptions: how you treat an urgent request that arrives mid-task, and how you keep a written list so priorities remain visible to the team.

Follow-up

Two stakeholders each say their request is top priority. What do you do?
What did you drop in your example, and who signed off on that?
How do you handle a new urgent request halfway through a sprint?

Tell me about a time you received constructive feedback. How did you respond?

Medium · Behavioral / Leadership

Approach

Choose feedback that was real and slightly uncomfortable, such as unclear pull request descriptions or taking on too much silently. Avoid a strength in disguise.
State what you heard and your first reaction honestly, then how you checked your understanding, perhaps by asking for a concrete example.
Describe the specific change you made (a template for pull requests, earlier status updates, asking for review sooner) and how you kept it up.
Give evidence it worked, such as later review comments or a teammate's remark, and mention how you now ask for feedback on purpose.

Follow-up

Did you agree with the feedback when you first heard it?
How do you ask for feedback before anyone volunteers it?
Have you given similar feedback to someone else, and how?