Api Nation Software Engineer Interview Guide

The 60-second brief

Candidate material describes Api Nation's offering as a platform that connects various APIs so users can integrate them, with customers ranging from small businesses to larger enterprises. Reports describe the Software Engineer job as building and maintaining application features that integrate APIs, agreeing requirements with product and design teams, taking part in code reviews, and resolving technical problems in production. Little else about the product is reported, so preparation should centre on the engineering skills this kind of integration work uses: HTTP and REST, handling failures between services, SQL, and clear code.

This guide is for candidates interviewing for the Software Engineer role at Api Nation. It covers the three reported rounds (Initial Screening, Technical Interviews, Behavioral Interviews), the twenty questions candidates report, and how to answer each type. Candidates describe the requirements as JavaScript or Python, experience with API development and integration, and roughly 2-5 years in software roles, with cloud services (AWS, Azure), front-end frameworks (React, Angular) and automated testing as extras. Reported topic tags are C#, REST APIs, JavaScript and API design.

Candidates report three rounds over roughly 3-5 weeks: an Initial Screening, Technical Interviews that may include coding assessments and system design discussions, and Behavioral Interviews on interpersonal skills and cultural fit.

Focus areas

• C#
• REST APIs
• JavaScript

Overview

The work candidates describe is integration work: building features that connect APIs, agreeing requirements with product and design, reviewing code, and fixing problems in production. That shapes the reported questions. Several are about how services talk to each other: how RESTful APIs work, a feature that integrates two different APIs, the benefits and drawbacks of microservices, and synchronous versus asynchronous programming. Others are about keeping a running system healthy: optimizing a slow SQL query, finding a performance bottleneck in an application, and troubleshooting a system failure. Prepare these as explanations you can walk through step by step, including what happens when a call fails halfway.

The reported coding questions are classic fundamentals: reverse a string, implement binary search, find two numbers that add up to a target, approach a sorting problem, and detect a cycle in a linked list. They are short, so precision matters: correct edge cases, a stated time and space complexity, and a reason for each data structure you pick. Choose one language and drill it. Reported topic tags are C#, REST APIs, JavaScript and API design, and the role requirements candidates describe list JavaScript and Python, so decide which language you will code in and ask the recruiter whether the choice is yours.

Seven of the twenty reported questions are behavioral: a production debugging story, competing priorities, a difficult team member, clear communication, tight deadlines, a contribution to team success, and what motivates quality work. Reports also mention persuading others to accept your viewpoint and handling feedback from teammates. Build a small set of real stories that each carry a decision, a trade-off and a result, and reuse them across prompts. The topic weighting behind these reports comes from a single reported loop, so treat it as a pattern to prepare for rather than a fixed list.

The Api Nation interview process

Candidates report three rounds over about 3-5 weeks. The descriptions below are what candidates describe, so confirm the exact format and order with your recruiter.

Initial Screening

Candidates describe the Initial Screening as a first check of basic qualifications and fit for the role. Little else is reported about its format, so prepare for a conversation about your background. Be ready to present your CV as a story that centres on API work: which APIs you integrated, how you authenticated, what went wrong in production and what you changed. Also be ready to state which languages you are strongest in, since candidates describe the role as JavaScript or Python with API development and integration experience. Use the call to ask what the Technical Interviews will contain and in which language you will code.

What to demonstrate

• Basic qualifications measured against the requirements candidates describe: JavaScript or Python, API development and integration
• Fit for the role, based on how clearly you explain your own experience
• Roughly 2-5 years of software experience, as the role is described

How to prepare

• Outline your CV around two or three API integration or backend projects, each with the technology, your part and the result
• Prepare a short answer on why this integration-focused role interests you, tied to work you have actually done
• Refresh your strongest language (C#, JavaScript or Python) so you can discuss it without hesitation
• Write down the questions you want answered about the later rounds: format, language, and whether a design discussion is included

Technical Interviews

Candidates report several technical interviews that may include coding assessments and system design discussions. A coding assessment is where short algorithm problems are practised, such as reversing a string, binary search, two numbers that add up to a target, a sorting problem and detecting a cycle in a linked list. A design discussion is where the API and architecture questions fit: how RESTful APIs work, integrating two different APIs, microservices trade-offs, synchronous versus asynchronous programming, SQL tuning and performance bottlenecks. Think aloud, state assumptions, give complexity for code, and name failure cases for designs.

What to demonstrate

• Working code on fundamental data-structure and algorithm problems, which is what a coding assessment inherently tests
• Reasoning about design trade-offs in a system design discussion
• Depth in the technical topics candidates report: C#, REST APIs, JavaScript and API design
• How you approach a performance issue or failure in a running application

How to prepare

• Write solutions to the five reported coding questions in your chosen language and test each on empty, single-element and duplicate inputs
• Prepare a structured REST explanation: resources, verbs, status codes, idempotency, statelessness, pagination and versioning
• Sketch an integration of two APIs with authentication, timeouts, retries with an idempotency key, rate limits and partial failure
• Write a short list of microservices benefits and drawbacks, plus a numbered investigation sequence for a slow query or a slow endpoint

Behavioral Interviews

Candidates describe the Behavioral Interviews as a stage on cultural fit and interpersonal skills, driven by behavioral questions. The reported prompts include working with a difficult team member, communicating clearly with a team, prioritizing under tight deadlines, contributing to a team's success, and what motivates you to deliver high-quality work. Production debugging and balancing competing priorities are also reported as questions to prepare. Approach it with a short list of real stories, each showing the situation in a sentence or two, the decision you made and why, and a result you can support.

What to demonstrate

• Interpersonal skills and cultural fit, as the stage is described
• How you work with others and contribute to a team's results
• How clearly you explain your own decisions and what you learned

How to prepare

• Pick four or five real stories and map each to several reported prompts so you can adapt rather than recite
• For each story, write the situation, your specific decision, the trade-off and a result you can verify
• Prepare an example of persuading someone to accept your viewpoint and one of acting on feedback from a teammate
• Rehearse the production debugging story aloud until the sequence of checks and the root cause are crisp

Where candidates lose points

Explaining REST as "GET and POST over HTTP" and stopping there

Prepare a fuller answer: resources and URLs, the verbs and which are safe or idempotent, status codes, statelessness, pagination, versioning and error bodies. Have a small concrete endpoint example ready.

Describing an integration of two APIs on the happy path only

Walk through failure partway: a timeout after the first call succeeds, a retry that could apply the change twice, expired credentials and rate limits. Mention idempotency keys, bounded retries with backoff and how a caller learns the final state.

Writing the coding answer silently, then missing edge cases such as an empty string, an off-by-one in binary search, duplicate values in the two-number sum or a cycle that starts at the head

Think aloud, state the complexity, and trace your code on empty, single-element, duplicate and cyclic inputs before saying you are done.

Answering the slow SQL query and performance bottleneck questions with "add an index" or "add a cache" before measuring anything

Describe the sequence: reproduce, measure, read the query plan or profile, identify the actual bottleneck, change one thing, then measure again. Mention how you would confirm it was not a different cause.

Telling behavioral stories in terms of "we" with no personal decision or result, or telling the production debugging story without a root cause

For each story write the situation, your decision, the trade-off and a result you can support. Include the root cause and what you changed to prevent a repeat.

Questions & practice

Explain the difference between synchronous and asynchronous programming.

Medium · Technical / Domain Questions

Approach

1. Define the two by what the caller does: a synchronous call blocks until the result returns, so the next line waits; an asynchronous call starts the operation and hands back a promise, task or callback, so the caller keeps running and resumes when the result is ready.
2. Separate asynchronous from parallel. JavaScript runs your code on one thread with an event loop: I/O is handed to the runtime, and the continuation is queued when it finishes. CPU-heavy work still blocks that loop. In C#, awaiting an I/O-bound Task frees the thread; Task.Run moves CPU work to the thread pool.
3. Tie it to API work: a server waiting on an outbound HTTP call can serve other requests meanwhile. Independent calls started together with Promise.all or Task.WhenAll take about as long as the slowest call, not the sum of all of them.
4. Name the pitfalls. A rejected promise or faulted task needs try/catch around the await or it goes unhandled. Blocking on async code with .Result or .Wait() in C# can deadlock under a synchronization context. Completion order is not guaranteed.
5. Know the queue order in JavaScript: promise callbacks are microtasks and run before timer callbacks (macrotasks), even when the timer delay is 0.

Follow-up

• What does this print: console.log(1); setTimeout(() => console.log(2), 0); Promise.resolve().then(() => console.log(3)); console.log(4)? (Answer: 1, 4, 3, 2.)
• You need to call a third-party API for 10,000 records without hitting its rate limit. How do you cap how many requests run at once?
• When does making code async give no speed-up at all?

Write a function to reverse a string.

Medium · Coding / Algorithms

Approach

1. Clarify the language semantics first. Strings are immutable in Python, JavaScript, Java and C#, so a reversal builds a new string: copy to a character array, swap with two pointers (i from the start, j from the end, while i < j), then build the string. That is O(n) time and O(n) space for the copy.
2. Know the idioms and their limits. Python s[::-1]. JavaScript s.split('').reverse().join('') splits UTF-16 code units, so it breaks emoji made of surrogate pairs; [...s].reverse().join('') iterates by code point but still breaks combining marks and multi-code-point emoji. C# Array.Reverse on ToCharArray() has the same UTF-16 caveat.
3. For true user-perceived characters, reverse grapheme clusters: Intl.Segmenter in JavaScript, StringInfo text elements in .NET.
4. Avoid building the result with s = c + s in a loop. In Java or C# each concatenation copies the string, which makes it O(n^2); use a StringBuilder or a char array. A recursive version uses O(n) stack depth and can overflow on long input.
5. Edge cases: empty string, one character, null input, and strings with whitespace or Unicode.

Follow-up

• Reverse the order of words in a sentence in place. (Reverse the whole string, then reverse each word.)
• How does your function behave on a string containing an emoji or an accented letter written as a base letter plus a combining mark?
• Use the same two-pointer idea to check whether a string is a palindrome, ignoring case and punctuation.

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

1. State the precondition: the data is sorted, or more generally the predicate is monotonic. That gives O(log n) time and O(1) space iteratively, versus O(n) for a linear scan.
2. Write the classic version with a clear invariant: lo = 0, hi = n - 1, while lo <= hi compute mid = lo + (hi - lo) / 2 (avoids integer overflow in Java or C#), return mid on a match, otherwise set lo = mid + 1 or hi = mid - 1; return -1 when the loop ends.
3. Know the lower-bound variant on a half-open range: lo = 0, hi = n, while lo < hi, if a[mid] < target then lo = mid + 1 else hi = mid. It returns the first index with a[i] >= target, which is also the insertion point and handles duplicates.
4. Test the edges: empty array, one element, target below the minimum or above the maximum, duplicates. Also check that each step shrinks the range, because setting lo = mid with lo < hi can loop forever.
5. Extend it to searching on the answer: minimum capacity, smallest feasible value, integer square root. Any monotonic yes/no predicate over a range can be binary searched.

Follow-up

• Find the first and last position of a target in a sorted array that contains duplicates.
• Search a sorted array that has been rotated at an unknown pivot.
• How do you binary search when you do not know the array's length? (Exponential search: double the bound until it passes the target, then binary search inside it.)

Given a list of numbers, find the two that add up to a specific target.

Medium · Coding / Algorithms

Approach

1. Clarify: return indices or values, whether exactly one answer is guaranteed, whether the same element may be used twice, whether negatives or duplicates can appear, and whether the input is sorted.
2. Brute force checks every pair: O(n^2) time, O(1) space.
3. Optimal unsorted solution, one pass with a hash map from value to index: for each x at index i, compute need = target - x; if need is in the map, return [map[need], i]; otherwise store map[x] = i. Average O(n) time, O(n) space. Checking before inserting stops an element pairing with itself and still handles [3, 3] with target 6.
4. If the input is sorted, use two pointers from both ends: if the sum is too small move left forward, if too big move right back. That is O(n) time and O(1) space. Sorting first costs O(n log n) and loses the original indices unless you sort (value, index) pairs.
5. Edge cases: no valid pair (return an empty result or -1 as agreed), arrays shorter than two, and returning indices in the order asked.

Follow-up

• Return all unique pairs that sum to the target, with no duplicate pairs.
• Extend it to three numbers summing to a target. (Sort, then two pointers for each first element: O(n^2).)
• The numbers arrive as a stream too large to keep in memory. What changes?

Explain how you would approach solving a sorting problem.

Medium · Coding / Algorithms

Approach

1. Clarify before choosing: how many items, what the key is, whether equal keys must keep their order (stability), memory limits, whether the input is nearly sorted, the value range, and whether it fits in memory.
2. Default to the built-in sort and know its properties. Python uses Timsort (stable, O(n log n) worst case, O(n) on already-sorted input). Java sorts objects with stable TimSort and primitives with dual-pivot quicksort. C# Array.Sort and List.Sort use unstable introsort, while LINQ OrderBy is stable. JavaScript's sort is stable since ES2019 but compares as strings by default: [10, 9, 1].sort() gives [1, 10, 9], so pass (a, b) => a - b.
3. Compare the algorithms by their deciding properties. Merge sort: O(n log n) worst case, stable, O(n) extra memory. Quicksort: O(n log n) average, O(n^2) worst case, in place, unstable. Heapsort: O(n log n) worst case, O(1) extra, unstable. Insertion sort: O(n^2) in general, but O(n + inversions), so fast on small or nearly sorted input.
4. Comparison sorts cannot beat Ω(n log n). For integers in a small range k, counting sort is O(n + k); radix sort handles fixed-width keys in linear time for a fixed key width.
5. Check whether a full sort is needed at all: the top k items via a heap cost O(n log k), and quickselect finds the k-th element in O(n) on average. For data larger than memory, use an external merge sort: sort chunks, then k-way merge.

Follow-up

• Sort records by last name, then first name. How do you do it with one sort, and how with a stable sort used twice?
• Sort ten million integers that all lie between 0 and 1,000. What is the fastest approach?
• The file to sort is 50 GB and you have 4 GB of RAM. Walk through it.

Can you code a simple algorithm to detect cycles in a linked list?

Medium · Coding / Algorithms

Approach

1. Use Floyd's tortoise and hare: slow moves one node per step, fast moves two. If fast or fast.next becomes null, the list ends and has no cycle; if slow and fast ever point to the same node, there is a cycle. O(n) time, O(1) space.
2. Explain why it terminates: once both pointers are inside the cycle, the gap between them shrinks by one node each step, so fast cannot jump over slow and they meet within one lap.
3. Give the simpler alternative and its cost: a hash set of visited nodes compared by reference, not by value. Also O(n) time, but O(n) extra space.
4. Find where the cycle starts: after the meeting, move one pointer back to head and advance both one step at a time; they meet at the cycle's entry node. For the cycle length, hold one pointer still and count steps until the other returns.
5. Edge cases: empty list, a single node with no cycle, a single node pointing to itself, a cycle that starts at the head. Check fast != null before fast.next != null in the loop condition.

Follow-up

• Return the node where the cycle begins and explain why resetting one pointer to the head finds it.
• Remove the cycle so the list becomes linear again.
• Use the same idea to find the duplicate in an array of n + 1 integers whose values are all between 1 and n, without modifying the array.

How would you optimize a slow SQL query?

Medium · Technical / Domain Questions

Approach

1. Measure before changing anything. Get the real plan with EXPLAIN ANALYZE (or the actual execution plan in SQL Server) and look for full scans on large tables, estimated rows far from actual rows, nested loops over many rows, and sorts or hashes spilling to disk.
2. Match an index to the predicates. In a composite index, put equality columns first and the range or sort column last. Add a covering index (INCLUDE columns) when an index-only lookup would avoid reading the table. Low-selectivity columns rarely get used on their own.
3. Make predicates sargable. WHERE DATE(created_at) = '2026-01-01' cannot use a plain index on created_at; rewrite it as created_at >= '2026-01-01' AND created_at < '2026-01-02'. Watch for implicit type casts, and remember LIKE '%term' cannot use a B-tree index.
4. Cut the work: select only the columns you need, filter before joining and aggregating, replace a correlated subquery with a join or EXISTS, and page with keyset pagination (WHERE id > last_seen ORDER BY id LIMIT n) instead of a large OFFSET.
5. Check statistics (run ANALYZE / UPDATE STATISTICS when they are stale), then re-run the plan to prove the change worked. Every new index slows writes and takes space, so weigh that on write-heavy tables. A cache or materialized view is the last resort.

Follow-up

• You added an index and the planner still scans the whole table. What are the possible reasons?
• Why does OFFSET 100000 get slower as the offset grows, and what would you use instead?
• The query is fast in staging and slow in production. What differs, and how do you check?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

• A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
• The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

What are the benefits and drawbacks of microservices architecture?

Medium · Technical / Domain Questions

Approach

1. Benefits: each service is deployed and scaled on its own, a team can own one service end to end, failures can stay contained when the boundaries are right, and each service can choose its own storage or runtime.
2. Drawbacks, starting with the network: an in-process call becomes a remote call that adds latency and can fail partway, so every call needs timeouts, retries with backoff and often a circuit breaker.
3. Data is the hardest part. With a database per service there are no cross-service ACID transactions, so multi-step work uses sagas with compensating actions or an outbox, and readers must accept eventual consistency. A shared database between services produces a distributed monolith.
4. Operating cost: service discovery, per-service CI/CD, distributed tracing with correlation IDs, centralised logs, and versioned contracts between services.
5. Give a decision rule. With a small team or an unclear domain, start with a modular monolith. Split along bounded contexts when parts need different scaling or release cadence; for example, a worker that calls third-party APIs can then be scaled and rate-limited separately from the user-facing API.

Follow-up

• An operation updates data owned by two services. How do you keep them consistent if the second step fails?
• One request is slow and passes through five services. How do you find the one at fault?
• When would you merge two services back together?

Can you explain how RESTful APIs work?

Medium · Technical / Domain Questions

Approach

1. REST models the system as resources identified by URIs (/customers/42/orders) and acts on them with HTTP methods. GET is safe and idempotent; PUT (full replace) and DELETE are idempotent; POST creates and is not idempotent; PATCH is a partial update and is not guaranteed to be idempotent.
2. It is stateless: each request carries everything the server needs, including the auth token, so any instance can serve it and the service scales horizontally.
3. Use status codes precisely: 200, 201 with a Location header, 204, 400 for malformed input, 401 (not authenticated) versus 403 (authenticated but not allowed), 404, 409 for conflicts, 429 for rate limiting, 5xx for server faults. Return one consistent error body shape.
4. Use HTTP caching: Cache-Control, plus an ETag that the client sends back in If-None-Match so the server can reply 304 Not Modified. ETag with If-Match also gives optimistic concurrency on updates.
5. Cover the design details: plural noun paths, filtering and sorting via query parameters, cursor pagination for large or changing lists, versioning (/v1 in the path or a header), and an Idempotency-Key header so a retried POST is not applied twice. Contrast REST briefly with RPC and GraphQL.

Follow-up

• What is the difference between PUT and PATCH, and is PATCH idempotent?
• A client's POST times out and it retries. How do you stop a duplicate being created?
• How do you change a response field without breaking existing clients?

How would you approach optimizing a performance bottleneck in an application?

Medium · Problem-Solving / Case Studies

Approach

1. Pin the symptom down in numbers: which endpoint or job, p50 versus p95/p99 latency or throughput, since when, and under what load. Reproduce it with a load test so you have a baseline.
2. Measure where the time goes before guessing. Tracing or APM splits a request into app code, database and outbound calls. A CPU profiler or flame graph covers CPU-bound paths. Check saturation too: CPU, memory and GC pauses, connection and thread pool exhaustion.
3. Check the usual causes: N+1 queries (batch them or join), missing indexes, outbound calls made one after another that could run concurrently, repeated calls to a slow external API (cache with a TTL), oversized payloads (paginate or compress), and lock contention.
4. Fix the biggest contributor first and re-measure against the baseline. Removing a step that takes 10% of the request time can save at most 10% (Amdahl's law).
5. Stop it coming back: add a performance test for the hot path, plus a dashboard and an alert on p99, not only on the average.

Follow-up

• The median latency is fine but p99 is ten times worse. What does that suggest, and where would you look?
• How do you tell whether a slow endpoint is CPU-bound or waiting on I/O?
• You add a cache and it fixes latency. What new problems does it bring?

If you were given a project with limited requirements, how would you proceed?

Medium · Problem-Solving / Case Studies

Approach

1. Start from the problem, not the solution: who the users are, what outcome the project should produce, and how success will be measured. Ask about the constraints too: deadline, scale, the systems or APIs it must integrate with, security or compliance.
2. Write your assumptions down and get the stakeholder to confirm or correct them in a short one-page outline before building. That turns vague requirements into something people can disagree with.
3. Build the smallest end-to-end slice that tests the riskiest assumption first, for example one real integration call through to one visible result, and show it to users early.
4. Design for change where it is cheap: clear interfaces around the parts most likely to move, feature flags, and no heavy infrastructure before the need is proven. Note which decisions are hard to reverse and settle those deliberately.
5. Keep a running list of open questions and re-scope explicitly when answers arrive, so a change of requirements is a visible decision rather than silent scope creep.

Follow-up

• The stakeholder is unavailable for two weeks. What do you do in the meantime?
• How do you decide what is out of scope for the first version?
• Tell me about a time an assumption you made turned out to be wrong. What did it cost?

Provide a solution for a feature that requires integrating two different APIs.

Medium · Problem-Solving / Case Studies

Approach

1. Clarify the flow first: which system is the source of truth, which direction data moves, whether the feature is a live request or a background sync, the expected volume and latency, and how each API authenticates (API key, OAuth 2.0 with token refresh).
2. Structure: one client or adapter per external API behind an internal interface, each mapping its schema into a single internal model. Store the ID mapping between the two systems. Keep credentials in a secret store.
3. Reliability: set a timeout on every call. Retry with exponential backoff and jitter only when the call is idempotent or carries an idempotency key. Respect 429 responses and Retry-After. Add a circuit breaker for a failing dependency. A write that spans both APIs cannot be one transaction, so persist the job state and use compensating actions.
4. Sync mechanics: page through the source API with its cursor, sync incrementally using an updated-since filter or webhooks, verify webhook signatures, and deduplicate by event ID, because webhooks can arrive twice or out of order.
5. Make it observable and testable: a correlation ID across both calls, metrics on error rate and latency per API, contract tests against recorded responses, and a plan for when either API changes version.

Follow-up

• The second API is down for an hour. What does the user see, and what happens to the work in flight?
• The same webhook arrives twice. How do you make sure it is applied once?
• You must sync one million records through an API limited to 100 requests per minute. How do you plan it?

Describe a situation where you had to troubleshoot a system failure. What steps did you take?

Medium · Problem-Solving / Case Studies

Approach

1. Choose an incident where you drove the investigation. Open with the impact in one or two sentences: what broke, who was affected, how you found out.
2. Show that you mitigated first: a rollback, failover, feature flag or traffic shed to stop the damage, before searching for the root cause.
3. Walk through the diagnosis in order: build a timeline of what changed (deploys, config, traffic, a dependency), narrow the scope with metrics, logs and traces, form one hypothesis at a time, and test it or bisect until the cause is confirmed.
4. Separate the trigger from the root cause. A deploy or traffic spike may have exposed a bug that was already there. Say how you proved the cause, for example by reproducing it or by watching the failure stop after the fix.
5. Close with what changed afterwards: a test, an alert that would have caught it sooner, a runbook entry or a postmortem action, ideally with a measured result. A story where you only watched someone else fix it, or with no verified cause, weakens the answer.

Follow-up

• How did you know the fix addressed the cause and not just the symptom?
• What alert or check would have caught this earlier?
• What would you do differently if it happened again tomorrow?

Suggested practice checklist

Built from the three reported rounds and the reported questions: coding fundamentals first, then REST and integration design, then performance, SQL and failure analysis, and finally behavioral stories with a full rehearsal.

Day 1 — Map the loop and choose your language

• Write out the three reported rounds (Initial Screening, Technical Interviews, Behavioral Interviews) and note which question types each format can cover.
• Decide which language you will code in (C#, JavaScript or Python, the languages candidates and the role description point to) and note any syntax you hesitate on.
• Outline a short CV story built around your API integration or backend work.

Deliverable: A one-page map of the three rounds, a cheat sheet for your chosen language, and a CV story outline centred on API work.

Day 2 — Coding fundamentals: strings, search, hashing

• Write reverse a string in place on a character array and note the Unicode and empty-string cases.
• Implement binary search iteratively, then the lower-bound variant, and trace the loop invariant on a one-element and a two-element array.
• Solve the two-numbers-that-add-up-to-a-target problem with a hash map, then with two pointers on a sorted array, and state the complexity of each.

Deliverable: Three working solutions with stated time and space complexity and a list of edge-case tests for each.

Day 3 — Sorting, linked-list cycles and async

• Prepare a sorting answer that names merge sort, quicksort and insertion sort, and compares stability, memory use, worst case and nearly-sorted input.
• Code cycle detection in a linked list with a hash set and with fast and slow pointers, then extend it to find where the cycle starts.
• Write a spoken explanation of synchronous versus asynchronous execution using your language's mechanism (async/await and tasks in C#, promises and the event loop in JavaScript).

Deliverable: A comparison table of sorting algorithms, two cycle-detection implementations, and an outline of your sync versus async explanation with an example.

Day 4 — REST and integrating two APIs

• Write a REST explanation covering resources, verbs, idempotency, status codes, statelessness, pagination and versioning, with a small endpoint example.
• Design a feature that calls two different APIs: draw a sequence diagram with authentication, timeouts, retries using an idempotency key, rate limits and a partial-failure path.
• Say aloud how the caller discovers the final state of a request that timed out.

Deliverable: A REST cheat sheet and a sequence diagram of the two-API feature with its failure paths marked.

Day 5 — Architecture, performance and SQL

• Write the benefits and drawbacks of microservices as a two-column table and add the conditions under which you would keep a single deployable.
• Write a numbered investigation sequence for an application performance bottleneck: measure, profile, isolate, change one thing, measure again.
• Take one slow query you know, read its plan, and write what you would change and how you would verify it.

Deliverable: A microservices trade-off table, a bottleneck investigation checklist, and a written explanation of one SQL exercise.

Day 6 — Failure analysis, vague requirements and first stories

• Outline a troubleshooting answer: what changed, how you narrowed the cause, how you proved it and what you did to prevent a repeat.
• Write the questions you would ask and the assumptions you would test when handed a project with limited requirements.
• Write story outlines for the production debugging, competing priorities and difficult team member prompts, and rehearse each aloud.

Deliverable: A troubleshooting outline, a list of clarifying questions for a vague project, and three rehearsed story outlines.

Day 7 — Remaining stories and a full rehearsal

• Write story outlines for communication strategies, prioritizing under tight deadlines, contributing to team success, and what motivates you to deliver high-quality work.
• Add one example of persuading someone to accept your viewpoint and one of acting on feedback.
• Run a full rehearsal in loop order: your CV story, one coding problem, one API design walk-through, then three behavioral answers.

Deliverable: Four more story outlines, two extra examples, and notes from one full spoken rehearsal with the weakest answer marked for revision.

Stories to prepare for the behavioral interviews

Candidates describe the Behavioral Interviews as a stage on interpersonal skills and cultural fit. For a role that integrates APIs, work with product and design teams, and troubleshoots production, build stories around debugging, prioritization, disagreement and teamwork. Also rehearse the other reported prompts: what motivates you to deliver high-quality work, persuading others to accept your viewpoint, and handling feedback from teammates. Each story needs your decision, a trade-off and a result you can support.

Describe a time you debugged a complex issue in production.

Medium · Technical / Domain Questions

Approach

1. Choose an incident where you ran the investigation or made the key call. Open with the user-visible symptom in a sentence or two (errors on an endpoint, slow responses, wrong data) and how you learned of it: an alert, a ticket or a customer report.
2. Narrate the sequence in order. If it applied, say what you did first to limit damage (rollback, feature flag, rate limit). Then show how you narrowed it: what changed (deploy, config, dependency, traffic), what the logs, traces or metrics showed, and what each check ruled out.
3. Name the root cause precisely and separate the trigger from the cause, for example a downstream API timing out and retries piling up, a missing index, or a race on shared state. Use your real cause, not a tidy invented one.
4. Finish with the fix and the prevention: a regression test, an alert, a runbook or a follow-up change, plus a measured result such as the error rate returning to baseline. Only quote numbers you actually have.
5. The mistake that sinks this story is a fix found by luck or a cause blamed on another team. Show a reasoned path from symptom to proof.

Follow-up

• How did you confirm the root cause rather than just seeing the symptom disappear?
• What monitoring would you add so you catch the same failure earlier?
• What was the riskiest action you considered while it was live, and why did you take it or reject it?

Discuss a time when you had to balance competing priorities in a project.

Medium · Problem-Solving / Case Studies

Approach

1. Pick a story with a genuine trade-off: two deliverables with different owners, such as a product feature against a production fix or a deadline-bound change against cleanup. Name both items and who wanted each.
2. Show your method: compare the cost of delay, effort and dependencies for each, and ask the requesters to rank when you can. When two stakeholders conflict, escalate to the person who owns the priority call rather than choosing silently.
3. Describe the decision and how you communicated it: what you deferred, who you told, and what you negotiated (a smaller slice, a moved date, shared work).
4. Give the result honestly, including the cost of what you deferred and what you would change next time.
5. Avoid two failure patterns: claiming you did everything with no trade-off, and saying yes to every request without saying what slipped.

Follow-up

• What if both stakeholders insisted their item was the top priority?
• What did you drop, and how did its owner react?
• How would you handle the same situation today?

Describe a time when you had to work with a difficult team member. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Describe a real friction in terms of behavior, such as late reviews, dismissive code review comments or missed handoffs, not a personality label. Keep the tone neutral so you do not come across as casting a villain.
2. Lead with the direct step: a private conversation where you asked for their view, listened, and learned what constraints or misunderstandings sat behind the behavior.
3. Show a concrete working change you agreed on, for example written review expectations, a shared ticket, an agreed API contract or a regular sync. Bring in a manager only after a direct attempt, or when the delay was blocking the team.
4. Report the outcome: how the work and the relationship went afterward. If it stayed unresolved, say how you contained the impact on delivery.
5. Include what you changed in your own behavior; a story where only the other person was at fault weakens the answer.

Follow-up

• What would you have done if they had not changed after that conversation?
• What part of the friction did you contribute to?
• How did you keep the project moving in the meantime?

What strategies do you use to ensure clear communication with your team?

Medium · Behavioral / Leadership

Approach

1. This asks for practices, so answer with three or four concrete habits, each with a short example: written decisions (design notes, clear pull request descriptions, ticket updates), asynchronous status updates, a short sync when blocked, and restating requirements to confirm shared understanding.
2. Tie it to integration work with product and design teams: share example request and response payloads or an API spec before building, and flag breaking changes early so the people depending on them are not surprised.
3. Adapt to your audience: engineers want detail, product partners want impact and options. Give one example of turning a technical constraint into a trade-off a product manager can decide on.
4. Back the answer with one real instance where a communication habit caught a misunderstood requirement or prevented a problem, with its result.
5. Avoid a generic "I communicate openly" answer with no mechanism behind it.

Follow-up

• Tell me about a time communication broke down. What did you change afterward?
• How do you deliver bad news, such as a date that is slipping?
• How do you keep teammates in other time zones aligned?

How do you prioritize your work when facing tight deadlines?

Medium · Behavioral / Leadership

Approach

1. State your method first: list the work, split it by user impact and dependency, estimate each piece, and separate must-have from deferrable. Cut scope before you cut quality on the core path.
2. Make it concrete with one case: the deadline, what shipped, what you deferred and how you got agreement from the product side.
3. Surface risk early. As soon as your estimate passes the date, tell stakeholders and bring options: reduce scope, move the date or add help, rather than letting the date slip silently.
4. Say what you protect under pressure, such as tests on the integration path, code review and a rollback plan, and how you tracked the deferred items afterward.
5. Do not let the answer become working longer hours. Show an explicit trade-off you made.

Follow-up

• What do you do when both the date and the scope are fixed?
• Which tests or checks do you keep when time is short, and why?
• Tell me about a deadline you missed and what you learned.

Share an example of how you contributed to a team’s success.

Medium · Behavioral / Leadership

Approach

1. Choose a team outcome such as a launch, a migration, an incident recovery or an onboarding improvement. State the team's goal in one sentence, then describe your own part precisely: use "I" for your actions and name teammates for theirs.
2. Prefer a contribution that helped others work faster: unblocking a colleague, building shared tooling or a test harness, improving the review process, documenting an integration or mentoring a new teammate.
3. Quantify with a figure you can defend, such as build time, defects found or a release delivered on time. Do not invent numbers.
4. Credit others accurately and say how you supported their work; claiming the whole result weakens the story.
5. The mistake that sinks it is a contribution so vague that the listener cannot tell what you personally did.

Follow-up

• What would the team have lacked if you had not been involved?
• Which part was a teammate's work, and how did you support it?
• How did you measure the impact?

What motivates you to deliver high-quality work?

Medium · Behavioral / Leadership

Approach

1. Avoid a values statement with no evidence. Name one or two real drivers, such as people depending on an integration that must not fail, or ownership of code you will have to run and debug, and connect each to a behavior.
2. Show the practices that come from them: tests around integration boundaries, careful error handling for external calls, code review, monitoring after release. Give one example where you chose quality over speed, or the reverse, on purpose.
3. Show proportion. A throwaway prototype needs less care than code many customers depend on, and saying so shows judgement rather than perfectionism.
4. Link it to the work candidates describe for this role: integrating APIs and fixing production problems make reliability a practical concern, not just a principle.
5. Avoid "I just care about quality" and avoid perfectionism that delays delivery.

Follow-up

• Tell me about something you shipped that you were not proud of.
• How do you balance quality against a deadline?
• What do you do when a reviewer asks for changes you disagree with?