Allergan Aesthetics Software Engineer Interview Guide

The 60-second brief

Candidates describe Allergan Aesthetics as a company with a portfolio of aesthetic products, where software engineers work on the systems that support those products and on digital initiatives that sit between technology and healthcare. The reported duties are designing and building software features, working out requirements with product managers, designers and quality assurance staff, reviewing teammates' code, diagnosing faults in systems that are already running, and keeping technical documentation current. Candidate reports do not name a specific product, team, internal platform or codebase, so prepare for the role in general engineering terms rather than guessing at the product.

This guide is for engineers applying for a Software Engineer position at Allergan Aesthetics. Candidates describe a minimum of two to three years of software experience, with at least one of Java, Python or C#, a web framework such as Angular or React, and Git. It covers the three reported rounds, the sixteen reported questions (experience and lifecycle, architecture, troubleshooting, coding, behavioral), the front-end topics candidates list (TypeScript, React, JavaScript), and a seven-day plan that connects them.

Candidates report three rounds over roughly 3-5 weeks: an HR screening, technical interviews that can include coding challenges and conversations with engineering leads, and final discussions about overall fit and company values.

Focus areas

• TypeScript
• React
• JavaScript

Overview

The reported questions for this role are mostly open-ended. Of the sixteen attached to this guide, five ask about your experience and the development lifecycle, three are about architecture and design patterns, two are troubleshooting and optimization scenarios, three are coding (reversing a linked list, binary search, explaining time complexity), and three are behavioral. Candidates describe these as patterns of questioning rather than a fixed list, so use them to decide what to rehearse, not what to memorize.

The stack that candidates list is a mix. The topic breakdown, which comes from a single reported loop, names TypeScript, React and JavaScript alongside strategic software architecture and code quality. The requirements candidates describe add Java, Python or C#, Angular as an alternative to React, and Git, with AWS or Azure, data structures and algorithms, and CI/CD as extras. In practice that means choosing one language to code in under pressure, being able to discuss one front-end framework in real depth, and having a clear account of how you version, test and ship code.

Because most questions invite a story or a method instead of a single correct answer, the main risk is a generic reply. Questions such as how you ensure code quality, which languages you prefer and why, or how you would optimize an existing system are answered well only when they are tied to something you actually did: the test layers you wrote, the bug you traced, the measurement you took before changing anything. Build those examples before the loop, and keep each one to a few sentences you can say aloud.

The role is described as cross-functional, working with product managers, designers and QA, and as including code review, debugging and documentation. That shapes the behavioral half of the loop: the conflict, motivation and leadership prompts are easier to answer well with examples that involve people outside your own engineering team. Candidates also say the final stage looks at overall fit and company values, so plan time for that stage and not only for coding.

The Allergan Aesthetics interview process

Candidates describe three stages over about 3-5 weeks. The descriptions are short, so the preparation below is built from the stage names, the question categories and the stack candidates list, not from a published format.

Initial Screening

Candidates describe the first stage as a screening conversation with HR that checks basic qualifications and fit for the role. No technical content is reported for it, so treat it as the stage where your background is matched to the requirements candidates describe: two to three years of software experience, one of Java, Python or C#, a web framework like Angular or React, and Git. Prepare a specific, concise account of your recent work and a few questions about the team and the stages that follow.

What to demonstrate

• Basic qualifications against the listed requirements: years of experience, languages, web frameworks, version control
• Fit for the role, as candidates describe the stage
• How clearly you summarise your own background

How to prepare

• Line up your experience against the requirements: which of Java, Python or C# you have shipped in, whether your framework work is React or Angular, and how you use Git day to day (branching, pull requests, resolving merge conflicts).
• Write a short account of your latest project covering what you built, your own part, the stack and the result, and practise saying it aloud until it holds together without notes.
• Prepare a truthful reason for wanting to build software around aesthetic and healthcare products, plus questions about how the technical interviews are organised and who you will meet.

Technical Interviews

Candidates describe a series of technical interviews rather than a single one. Coding challenges are possible, and some conversations are with engineering leads. The topics reported for this role are TypeScript, React, JavaScript, strategic software architecture and code quality. No platform, language restriction or duration is reported, so prepare for both formats: write working code from a blank editor for the coding questions (reversing a linked list, binary search, explaining complexity), and talk through experience, design and troubleshooting questions with a method you can state step by step.

What to demonstrate

• Working code with a complexity you can state, which any coding challenge inherently tests
• Depth in the stack candidates list: TypeScript, React and JavaScript, plus whichever of Java, Python or C# you choose to discuss
• Reasoning about architecture trade-offs and code quality, two topics reported for this role

How to prepare

• Write a singly linked list reversal (iterative, then recursive) and a binary search in your strongest language without autocomplete, test them on empty, one-element and duplicate-heavy inputs, and say the time and space complexity of each.
• Revise what you may be asked to explain in TypeScript and JavaScript: closures, promises and async/await, the event loop, generics, union types and narrowing, and in React hooks, state, effects and why a component re-renders.
• Prepare a code quality answer with layers (unit tests, component tests, type checking, linting, CI gates, code review) and one bug each layer caught, plus one architecture decision where you can name the alternative you rejected.
• Rehearse a troubleshooting sequence for a client-reported bug and an optimization sequence for a slow system: reproduce or measure first, narrow down, change one thing, verify.

Final Discussions

Candidates describe the last stage as final discussions about overall fit and alignment with company values. No coding or design format is reported for it. Candidate guidance names collaboration and innovation as the values to be ready to talk about. The behavioral questions in this guide (a conflict with a team member, what motivates you, a project where you showed leadership) are the type of material to have ready, along with any example of teamwork that involves product managers, designers or QA.

What to demonstrate

• Overall fit with the role and team, as candidates describe the stage
• Alignment with company values, with collaboration and innovation the themes candidates mention
• How you describe teamwork and your own contribution in past projects

How to prepare

• Write three stories in the order situation, your decision, result: a conflict with a teammate, a project where you steered others, and an episode that shows what drives you. Keep each story to your actions and decisions.
• Read the company's public material about its business yourself and decide, in your own words, why the work interests you. Avoid repeating slogans.
• Prepare questions for the people you meet: how engineers work with product managers, designers and QA, how code review is run, and how documentation is kept up to date.

Where candidates lose points

Naming languages and frameworks as a list when asked which you are most comfortable with and why

For every language or framework you name, prepare one project, one thing it handles badly and one bug you fixed in it. Choose the language you will code in for the linked-list and binary search questions and say so, then describe a second one briefly.

Answering the code quality question with 'I write tests and do code reviews' and stopping there

Name the layers: unit tests for logic, component tests for React or Angular views, strict TypeScript settings, linting, CI checks that block a merge, and a review checklist. Add one defect each layer caught and what you changed afterwards.

Writing the linked-list reversal or the binary search quietly, then missing edge cases or giving a vague complexity

Say your invariant aloud (previous, current, next for the list; low and high bounds for the search). Test an empty list, one node, an empty array, a missing target and duplicates. Use a midpoint like low + (high - low) / 2, and state O(n) time with O(1) extra space for the iterative reversal and O(log n) for the search.

Describing a real-time data processing design or an architecture trade-off as a list of components with no reasons

Start with requirements (volume, latency, ordering, whether losing an event is acceptable), then walk ingest, buffer, process and store. For each choice name what you gave up, what happens when a stage is slow or down, and how you would notice.

Telling conflict and leadership stories in which the team did everything and your own decision is invisible, or the other person is simply wrong

Write each story as situation, your decision, the evidence you used, the result and what you would change. State the other person's best argument. Use 'I' for what you decided and name teammates for what they did.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Iterative three-pointer reversal: prev = null and curr = head. While curr is not null: save next = curr.next, set curr.next = prev, then prev = curr and curr = next. Return prev as the new head. This is O(n) time and O(1) extra space.
2. Recursive version: reverse the rest of the list, then set head.next.next = head and head.next = null. It is still O(n) time but uses O(n) call-stack space, so a very long list can overflow the stack.
3. Edge cases to state and test: an empty list, a single node and two nodes. Make sure the old head's next ends up null, or the list contains a cycle.
4. Verify by walking the result and checking both order and length. For a doubly linked list, swap each node's prev and next pointers and return the last node visited.

Follow-up

• Reverse only the nodes between positions left and right, in one pass.
• Reverse the list in groups of k nodes, leaving any remainder as it is.
• Check whether a linked list is a palindrome using O(1) extra space.

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

1. State the precondition: a sorted array, or more generally a monotonic predicate. Iterative version: lo = 0 and hi = n - 1. While lo <= hi, compute mid, return it if a[mid] equals the target, otherwise move lo = mid + 1 or hi = mid - 1. Return -1 if not found. This is O(log n) time and O(1) space.
2. Compute mid as lo + (hi - lo) / 2. In Java or C#, (lo + hi) / 2 can overflow int for large indices. In JavaScript, use Math.floor because division returns a float.
3. Choose an interval convention and keep it consistent. With a closed interval [lo, hi], loop while lo <= hi. With a half-open interval [lo, hi), loop while lo < hi and set hi = mid. Mixing the two causes infinite loops or skipped elements.
4. Variants: lower bound (the first index where a[i] >= target) handles duplicates, and binary search on the answer space (for example the minimum capacity that meets a condition) works whenever the predicate is monotonic.
5. Test the edge cases: an empty array, a single element, a target below the minimum or above the maximum, and runs of duplicates.

Follow-up

• Find the first and last positions of a target in a sorted array that contains duplicates.
• Search for a target in a sorted array that has been rotated at an unknown pivot.
• Why does binary search on a linked list not run in O(log n) time?

Can you explain the time complexity of your solution?

Medium · Coding / Algorithms

Approach

1. Count work as a function of input size: nested loops multiply, sequential steps add, and constants and lower-order terms drop out. Always give space complexity too, including the recursion stack.
2. Apply this to the answers you just gave: iterative list reversal is O(n) time and O(1) space; binary search is O(log n) because the search range halves each step; a recursive solution adds O(depth) stack space.
3. Separate the worst, average and amortised cases. A hash map lookup is O(1) on average but O(n) in the worst case. Appending to a dynamic array is amortised O(1). Quicksort is O(n log n) on average and O(n^2) in the worst case.
4. Solve common recurrences: T(n) = 2T(n/2) + O(n) gives O(n log n), as in merge sort, and T(n) = T(n/2) + O(1) gives O(log n).
5. Point out hidden costs in library calls: repeated string concatenation in a loop can be O(n^2) with immutable strings, a Python list slice copies k elements, and JavaScript's Array.prototype.shift is typically O(n).

Follow-up

• What is the space complexity of the recursive version, and why?
• Can this problem be solved faster, and what lower bound applies to it?
• How would the complexity change if the input were a linked list instead of an array?

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

1. 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.
2. 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.
3. 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.
4. 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?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

• 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
• A dashboard runs this for 40 owners in one page load. What changes about the design?

Explain the software development lifecycle and your role in it.

Medium · Technical / Domain Questions

Approach

1. Name the phases in order (requirements, design, implementation, testing, deployment, maintenance) and say that in Agile they repeat every iteration rather than running once as in waterfall.
2. For each phase, give one thing you did yourself: wrote acceptance criteria with a product manager, wrote a short design doc, reviewed pull requests, added unit and integration tests to CI, shipped behind a feature flag, fixed a production issue.
3. Contrast the models. Waterfall fits fixed requirements with formal sign-off gates. Scrum or Kanban fits requirements that change. Where formal validation applies, each requirement needs a traceable test.
4. Show where quality checks sit early in the cycle (code review, automated tests on every commit, a staging environment, a rollback plan) instead of saving all testing for the end.
5. Close the loop: monitoring, user feedback and incident reviews from maintenance become new requirements for the next cycle.

Follow-up

• Where in the lifecycle were most of the defects you have seen introduced, and where were they caught?
• How does your process change when requirements change in the middle of a sprint?
• What does your team's definition of done include?

Describe a challenging technical problem you solved. What was your approach?

Medium · Technical / Domain Questions

Approach

1. Pick a problem with a measurable symptom (latency, error rate, memory growth, a data mismatch) and a cause that was not obvious, and say which constraint made it hard.
2. Walk through the diagnosis: the hypotheses you listed and the evidence that ruled each one out (a reliable reproduction, logs, a profiler, git bisect between a good build and a bad one).
3. Name the fix and one alternative you rejected, and give the trade-off that decided it (risk, effort, performance, maintainability).
4. Quantify the outcome with before-and-after numbers, say how you verified it, and mention the regression test or alert you added.
5. Keep the ownership clear: say 'I' for your own steps and name the parts others did.

Follow-up

• What did you try first that did not work, and what did it tell you?
• How did you confirm that the fix addressed the root cause and not just the symptom?
• What did you put in place so the problem would not recur?

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

Medium · Technical / Domain Questions

Approach

1. Name one or two languages you have shipped production code in, with the project and its scale. Rank them honestly instead of listing every language on your CV.
2. Explain the 'why' with concrete properties: static typing in TypeScript, Java or C# catches type errors at compile time, while Python's dynamic typing makes scripting and data work fast. Name the cost of each, such as verbosity against runtime errors.
3. Map your experience to the stack candidates describe for this role: Java, Python or C#, web frameworks such as Angular or React, and TypeScript and JavaScript among the reported topics. If one is a gap, say how you would close it.
4. Show depth in your strongest language by explaining one non-trivial behaviour, for example the JavaScript event loop, TypeScript's structural typing, Java generics and type erasure, or Python's GIL.

Follow-up

• What do you dislike about your strongest language, and how do you work around it?
• How does TypeScript's structural type system differ from Java's nominal one?
• In JavaScript, why does a resolved promise's callback run before a setTimeout(fn, 0) callback?

How do you ensure the quality of your code?

Medium · Technical / Domain Questions

Approach

1. Describe testing in layers: many fast unit tests for logic, integration tests at boundaries such as the database or APIs, and a few end-to-end tests for critical user flows. Say why the distribution is shaped that way.
2. Automate the checks: a linter (for example ESLint), a type checker (TypeScript in strict mode) and a formatter, run in a pre-commit hook and again in CI.
3. Code review: keep pull requests small, write a clear description, and say what you check as a reviewer (correctness, edge cases, naming, error handling, test coverage of the change).
4. Make merges depend on a green CI build. Treat coverage as a signal for untested areas, not a target to game.
5. Use production feedback: logging, error tracking and feature flags. When a bug escapes, add a regression test reproducing it before you fix it.

Follow-up

• How do you decide what not to test?
• How would you safely change a legacy module that has no tests?
• What do you do when a reviewer disagrees with your approach?

Discuss a time you had to learn a new technology quickly. How did you approach it?

Medium · Technical / Domain Questions

Approach

1. Open with the context: the technology, why it was needed, and the deadline. Separate what you had to learn before shipping from what could wait.
2. Give the learning sequence: official docs or tutorial, a throwaway spike to test the core idea, reading existing code that used it, and pairing with or asking someone experienced.
3. Show how you kept the risk down: a small first change, review by someone who knew the technology, and tests around the new code.
4. State the outcome with a concrete result, the mistake you made while learning, and how you fixed it.
5. Pick a technology close to this role if you can, such as moving to TypeScript or React, a new cloud service, or a CI/CD tool.

Follow-up

• How did you know you understood it well enough to ship production code?
• What would you do differently if you had to learn it again?
• How do you keep up with changes in your current stack?

How would you design a system to handle real-time data processing?

Medium · System Design / Architecture

Approach

1. Clarify requirements first: the event sources, the peak event rate, the latency target (sub-second or seconds), whether ordering matters, what delivery guarantee is needed, and how long data is kept.
2. Outline the pipeline. Producers write to a durable, partitioned log (Kafka, Kinesis, or a managed queue), keyed by an entity id so each key's events stay in order. A stream processor (Flink, Kafka Streams, or Spark Structured Streaming) does windowed aggregation. Results go to a low-latency serving store and to object storage or a warehouse for batch analysis.
3. Correctness: at-least-once delivery plus idempotent writes deduplicated by event id gives effectively-once results. Use event time with watermarks so late events land in the right window (tumbling or sliding).
4. Scaling and failure: consumers in a group scale up to the number of partitions. Checkpoint operator state so the processor can recover. Handle backpressure. Send poison messages to a dead-letter queue. Watch for hot partitions caused by skewed keys.
5. Observability and security: alert on consumer lag as the main health metric. If events carry personal data, encrypt them in transit and at rest, and keep identifiers out of event payloads where possible.

Follow-up

• How do you handle an event that arrives ten minutes late, for a window you already emitted?
• What happens when one key produces most of the traffic, and how would you rebalance it?
• How would you reprocess a day of data after fixing a bug in the aggregation logic?

Describe the trade-offs you consider when designing a software architecture.

Medium · System Design / Architecture

Approach

1. Start from requirements: what the system must do, plus the non-functional ones (latency, availability, consistency, security and compliance, cost, team size and skills). Every trade-off is judged against these.
2. Monolith or services: services give independent deploys and scaling, but they bring network failures, distributed transactions and more operations work. A modular monolith is often the right start, with a clear path to split modules out later.
3. Data and communication: strong or eventual consistency, SQL or NoSQL chosen by access pattern and transaction needs, synchronous calls or a queue (a queue decouples services and absorbs spikes but adds eventual consistency and harder debugging).
4. Build or buy: a managed cloud service against self-hosting, weighing operational burden, lock-in and cost at the expected scale.
5. Record decisions in short architecture decision records. Prefer choices you can reverse, and name the signal that would make you revisit each one.

Follow-up

• Tell me about an architecture decision you made that turned out wrong, and what you changed.
• When would you split a service out of a monolith?
• How do you keep data consistent when one business operation spans two services?

What design patterns have you used, and in what contexts?

Medium · System Design / Architecture

Approach

1. Pick two or three patterns you actually used. For each, give the context, the problem it solved and what it cost. Examples: Strategy to swap validation or pricing rules instead of growing if/else chains, Adapter to wrap a third-party API behind your own interface, Decorator to add caching or logging without editing the wrapped class.
2. Observer or publish/subscribe for event-driven updates: UI state changes, domain events, or message handlers. Mention the risk of hard-to-trace control flow.
3. Dependency injection for testability: depend on interfaces and inject fakes in tests. Angular has a built-in DI system. In React, composition and hooks usually replace inheritance-based patterns, and Context can supply dependencies.
4. Show judgment by naming a case where a pattern was overkill or harmful, such as a Singleton acting as hidden global state that made tests order-dependent, or an abstraction with only one implementation.
5. Tie each pattern to the principle it supports. Strategy keeps code open for extension and closed for modification, and an Adapter isolates the code from vendor changes.

Follow-up

• What is the difference between the Strategy and State patterns?
• Why is Singleton often treated as an anti-pattern, and when is it acceptable?
• How would you refactor a long switch statement on a type field using one of these patterns?

If you had to optimize an existing system, what steps would you take?

Medium · Problem-Solving / Case Studies

Approach

1. Define the goal and the metric before touching anything: which latency percentile (p95 or p99), throughput or cost. Record a baseline under realistic load.
2. Measure to find the bottleneck with a profiler, APM traces and database query plans (EXPLAIN ANALYZE). Use Amdahl's law to prioritise: if a component takes 10% of request time, removing it entirely gives at most about an 11% speedup.
3. Apply fixes roughly in order of cost: add missing indexes and remove N+1 query patterns, batch calls, paginate, cache hot reads with an explicit invalidation strategy, move slow work to an async queue, and scale horizontally only after that.
4. Change one thing at a time, load-test against the baseline, and roll out behind a flag or canary so a regression can be reverted quickly.
5. State the trade-offs: caching adds staleness and invalidation bugs, async processing adds eventual consistency, and every optimisation adds code to maintain.

Follow-up

• How would you detect an N+1 query problem in an existing service?
• What cache invalidation strategy would you choose, and what goes wrong with it?
• How do you confirm that the improvement held in production?

Given a scenario where a client reports a bug, how would you approach troubleshooting?

Medium · Problem-Solving / Case Studies

Approach

1. Triage before debugging: work out severity, the number of affected users and whether a workaround exists. Acknowledge the client, then collect exact steps, environment, app version, timestamps and any request or record ids.
2. Reproduce with the same version, data shape and browser or device. If you cannot reproduce it, correlate logs and traces by request id. Check recent deploys, configuration changes and dependency updates in that time window.
3. Narrow it down systematically. Isolate the layer (frontend, API, database or third-party service), toggle feature flags, and run git bisect between a known-good and a known-bad commit, which needs only about log2(n) builds.
4. Fix with a failing regression test first, get it reviewed, deploy (or roll back if the fix is not quick and the impact is high), then confirm with the client and with monitoring.
5. Close the loop with a short root-cause write-up, plus the logging or alerting that would have caught the bug before the client did.

Follow-up

• What do you do if you cannot reproduce the bug at all?
• The bug affects only one client. What would you compare between that client and everyone else?
• When would you roll back instead of fixing forward?

Suggested practice checklist

The plan follows the three reported rounds: background and lifecycle first, then coding and the TypeScript, JavaScript and React topics, then architecture and troubleshooting, then the stories for the final discussions. Each day produces something you can say or run.

Day 1 — Match your background to the requirements

• Build a table of the requirements candidates describe (Java, Python or C#; Angular or React; Git; AWS or Azure, algorithms and CI/CD as extras) against one piece of evidence each from your own work.
• Write your answer to the languages question: for your strongest language give two reasons, each tied to a project, and one weakness.
• Map your last project onto the development lifecycle (requirements, design, build, test, release, maintenance), marking your role at each stage and where product managers, designers and QA were involved.

Deliverable: A one-page requirements-versus-evidence table, a written languages answer and a written lifecycle walk-through of your last project.

Day 2 — Linked list, binary search and complexity

• Implement a singly linked list reversal iteratively and recursively in your chosen language, and test it on an empty list, one node and two nodes.
• Implement binary search for an exact match, then change it to return the first index at or above a target. Test duplicates, a missing target and a one-element array.
• Write out and say aloud the time and space complexity of each version, including the stack depth of the recursive reversal.

Deliverable: Tested code for both functions in your language and a short spoken script that states each complexity and explains why.

Day 3 — TypeScript and JavaScript fundamentals

• Write short typed examples: a generic function, a discriminated union handled with an exhaustive switch, and a narrowing check with a type guard.
• Write JavaScript examples for a closure-based counter, a debounce function, and the same request flow once with promise chains and once with async/await, including error handling.
• Predict the output order of a snippet that mixes synchronous code, a timer and a resolved promise, then explain microtasks versus timers aloud.

Deliverable: A file of five small examples, each with a one-sentence explanation you can say aloud.

Day 4 — React components and code quality

• Build a small React component with typed props that loads a list, shows loading and error states and filters it (build the Angular equivalent if that is your framework).
• Write tests for it at the component level: the loading state, an error response and a filter interaction.
• Write your answer to how you ensure code quality as layers, each with a real example from your work, and list three design patterns you have used with a place you would avoid each.

Deliverable: A working component with passing tests, a written code quality answer and a list of three patterns with when to use and avoid each.

Day 5 — Architecture and real-time processing

• Design a real-time data processing system on paper: list your clarifying questions, then ingest, buffer, process and store, with ordering, duplicate handling, back pressure and monitoring.
• Note where a managed cloud service on AWS or Azure would replace a component, if you have used one, and what you would still have to operate yourself.
• Write a trade-off sheet for the architecture question with at least five pairs (for example latency against throughput, consistency against availability, build against buy, monolith against services), each tied to a decision from your experience.

Deliverable: One diagram of the real-time design and a one-page trade-off sheet with a personal example for each pair.

Day 6 — Troubleshooting, optimization and learning fast

• Rehearse the client-reported bug scenario aloud as a sequence: get exact steps and versions, reproduce, check recent changes and logs, narrow down, fix, add a regression test, report back.
• Write the optimization answer as measure, find the biggest cost, change one thing, measure again.
• Write a short story for learning a new technology quickly: what you needed to ship, how you narrowed what to learn, and how you checked your understanding.

Day 7 — Behavioral stories and a full run-through

• Write four story outlines in the order situation, your decision, result: a challenging technical problem, a conflict with a team member, what motivates you, and a project where you showed leadership.
• Record yourself telling each story and cut anything that is about the team instead of your own decision.
• Run a mock with a peer covering one coding question, one design question and two stories, then list the questions you would ask the people you meet.

Deliverable: Four written story outlines, one recorded run-through with notes on what to cut, and a list of questions for the team.

Stories for the fit and values conversations

Candidates report that the HR screening checks fit and that the final discussions cover overall fit and company values. The behavioral prompts reported for this role are a conflict with a team member, what motivates you and a project where you showed leadership. The role works across product managers, designers and QA, so choose stories with people outside your own team where you can, and keep each one focused on a decision you made.

1. Discuss a time you had to learn a new technology quickly. How did you approach it?
2. Describe a challenging technical problem you solved. What was your approach?
3. Tell me about code review feedback you disagreed with and what you did next.

Describe a time you had a conflict with a team member. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Choose a disagreement with a concrete stake, such as an API contract, a code review comment you rejected, a release date or how much testing was enough. A personality clash with no decision attached gives you nothing to analyse.
2. Set the scene in two sentences, then state the other person's position in its strongest form. Showing that you understood their reasoning is what separates a resolved conflict from one you simply outlasted.
3. Describe your first move and why: a direct conversation, asking what risk they were protecting against, then a way to settle it on evidence, such as a small prototype, a benchmark, a test that reproduces the failure or a short trial of both options.
4. Close with the result, the working relationship afterwards and one thing you now do differently. A story that crosses disciplines (with a designer, a product manager or QA) suits this role. Avoid ending on 'my manager decided' as the only resolution.
5. Sample line: 'I disagreed with a teammate about validating in the client or the API. I asked which failure he was worried about, wrote a test that reproduced it, and the result moved the decision to server-side validation with a light client check.'

Follow-up

• What was the strongest part of the other person's argument?
• What would you do differently if the same situation came up again?
• What if the evidence had not settled it and they still disagreed?

What motivates you to succeed in your role?

Medium · Behavioral / Leadership

Approach

1. Treat it as a values-and-evidence question, not a slogan. Name two or three drivers, such as shipping something people depend on, getting better at a craft, or leaving a codebase easier to work in, and attach one real episode to each.
2. Show how the driver appears in daily work: the refactor you did without being asked, the bug you chased to its root cause, the documentation you updated after a change. Concrete behaviour can be checked; statements about passion cannot.
3. Connect to the role honestly. The reported duties are building features with product managers, designers and QA, reviewing code, debugging live systems and maintaining documentation. Pick the part that genuinely energises you, and say how you handle the part that energises you less.
4. If you cite healthcare or patient impact, support it with something specific you did or read, or it will sound rehearsed. Avoid an answer that rests only on pay, title or the brand name.

Follow-up

• What demotivates you at work, and what have you done about it?
• Tell me about a time your motivation dropped on a project. What did you do?
• How do you stay engaged on maintenance and bug-fixing work?

Can you provide an example of a project where you demonstrated leadership?

Medium · Behavioral / Leadership

Approach

1. Pick a project where you had influence without a manager title: a feature, a migration, a testing push or an onboarding effort. State the goal and why it needed coordination across people or teams.
2. Spell out your own decisions: how you split the work, what you chose not to do, how you got agreement from product, design or QA, and how you unblocked teammates. Use 'I' for your actions and name others' contributions separately so your share is clear.
3. Include a setback, such as a missed estimate, a disagreement or a teammate who was struggling, and show what you changed in the plan and how you kept people informed.
4. End with the outcome and what lasted, for example a review checklist or release routine the team kept using, then say what you would change. The common mistake is describing a project you took part in rather than one you steered.

Follow-up

• How did you handle a teammate who disagreed with your direction?
• What did you delegate, and what did you keep for yourself?
• How would you have led it if you had no say over the schedule?