CalOptima Software Engineer Interview Guide

The 60-second brief

CalOptima is a healthcare organization, and the Software Engineer role is described as building applications for its healthcare programs. Candidates describe that work as ranging from patient management systems to data analysis tools, done together with product managers, designers and other engineers. The requirements candidates list name Java, C# or Python, web frameworks such as React or Angular, and SQL or NoSQL databases. Cloud services such as AWS or Azure, DevOps tooling and healthcare-technology knowledge are listed as nice-to-have.

This guide is for software engineering candidates preparing for CalOptima. It covers the two reported stages, the 20 reported questions (languages and databases, debugging and REST, case-style discussions, coding and algorithms, and behavioral prompts) and a seven-day plan.

Candidates report two stages over roughly 2-4 weeks: a Phone Screen, then one or more In-Person Interviews with team members and possibly cross-functional partners. Reports say technical skill, problem-solving and cultural fit are all in play.

Focus areas

• Phone Interview
• In-Person Interview
• Interview Logistics

Overview

The reported question set is broad rather than deep in any one area. It opens with experience questions (which languages you have used, what you have done with databases), moves to RESTful APIs and debugging, adds case-style prompts about redesigning a confusing feature, scaling and integrating new technology, and includes classic coding items: reversing a linked list, implementing a search, choosing a sorting algorithm and stating complexity. Candidates describe the emphasis as real-world problem-solving rather than memorised facts, so every answer should be anchored in something you built or fixed.

The responsibilities candidates list are day-to-day engineering: designing and implementing features from user feedback, defining scope with product managers and designers, reviewing code, and joining stand-ups and sprint planning. That makes the behavioral questions (a difficult colleague, receiving feedback, mentoring a junior engineer, juggling projects, deciding quickly) part of the technical story. Prepare them with the same care as the linked-list code, and reuse the same projects across both.

Plan your preparation around the stack candidates name. Be ready to speak concretely about one or two of Java, C# or Python, about a relational and a non-relational store, and about a web front end if you have used React or Angular. If you have cloud, DevOps or healthcare-domain exposure, say so briefly; those are listed as nice-to-have, so do not claim depth you lack.

Reports also say some interviewers weigh cultural fit as much as technical skill, and mention community service in the context of the mission. Prepare one honest, specific reason you want to work on healthcare software, tied to a program or kind of user, rather than a recited mission statement.

The CalOptima interview process

Candidates describe two stages, and variation by team is mentioned. The reports do not tie specific questions to a stage, so prepare the whole question set for both.

Phone Screen

Candidates describe the Phone Screen as an initial call about your background and fit for the role. No question list is tied to it, so treat it as a conversation where your experience has to be clear and concrete. Be ready to talk through the languages you have used, the databases you have worked with and one project you can explain end to end, since those experience questions are among the reported ones. Have a short, honest answer ready for why healthcare software.

What to demonstrate

• How well your background lines up with the role as described to you
• Whether you can explain your own projects clearly, in a few sentences each
• Early signs of fit with the team and the mission

How to prepare

• Write a short spoken summary of your career that names the languages and data stores you have really used, and one project each
• Prepare the answers to the reported question about languages and the one about database experience, each ending in a specific project
• Write two or three questions to ask about the team, the applications it maintains and how engineers work with product managers and designers
• Have a precise answer to why this organization and healthcare software, without repeating its mission statement

In-Person Interviews

Candidates describe one or more In-Person Interviews with team members and possibly cross-functional partners. Reports say the loop mixes technical and behavioral assessment and that cultural fit can matter as much as technical skill. Prepare the full reported set: the debugging and REST explanations, the case-style prompts (confusing feature, scaling, performance, integrating new technology), the coding items (reverse a linked list, search, sorting, complexity) and the behavioral stories. Expect to explain trade-offs aloud, and to talk with people who are not engineers.

What to demonstrate

• Working code and clear complexity reasoning on the reported algorithm items
• How you structure debugging, performance and scalability problems
• How you communicate with teammates and cross-functional partners
• Teamwork, feedback and prioritisation shown through real stories

How to prepare

• Code reverse-a-linked-list iteratively and recursively in your strongest language, and test it on empty, one-node and two-node lists
• Rehearse a debugging sequence and a performance-analysis sequence on bugs you have actually fixed
• Write one REST resource design with verbs, status codes and idempotency, so the REST question has a concrete example behind it
• Prepare five STAR stories covering a difficult colleague, feedback, mentoring, prioritisation and a fast decision, and explain the technical parts in plain language for a non-engineer

Where candidates lose points

Naming Java, C# and Python without a project behind each

Prepare one project per language you list: what you built, one decision you made, one thing you would change. Drop any language you cannot discuss for a few minutes.

Losing the next pointer when reversing a linked list, or failing on short lists

Use three references (previous, current, next), advance them in a fixed order and state O(n) time and O(1) space. Test empty, one-node and two-node lists out loud before you say it is done.

Describing REST as any HTTP endpoint, or debugging as adding print statements

For REST, name resources, verbs, status codes, statelessness and idempotency, and give an endpoint you built. For debugging, state the sequence: reproduce, narrow the cause, test a hypothesis, fix, add a regression test.

Telling behavioral stories in team pronouns, with no personal action or result

Name what you decided and did, then the result. Prepare five distinct stories (difficult colleague, feedback, mentoring, prioritisation, fast decision) so none is stretched across two questions.

Treating the cultural-fit side as a recited mission statement

Reports say fit can weigh as much as technical skill, so prepare one specific, honest reason you want to build healthcare software, tied to a kind of user or program you looked up.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Iterative with three pointers: prev = null, curr = head; while curr is not null: next = curr.next, curr.next = prev, prev = curr, curr = next; return prev. O(n) time, O(1) extra space.
2. Recursive alternative: reverse the rest of the list from head.next, then set head.next.next = head and head.next = null, and return the new head. Still O(n) time, but O(n) call-stack space, so a long list can overflow the stack (CPython's default recursion limit is 1000).
3. Edge cases to state and test: empty list, a single node, two nodes. Make sure the old head ends with next = null, or the result contains a cycle.
4. Write the node class in your chosen language (Java, C# or Python), plus small helpers that build a list from an array and turn it back, so you can show input and output in the interview.

Follow-up

• Reverse only the nodes between positions m and n.
• 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 search algorithm in a programming language of your choice?

Medium · Coding / Algorithms

Approach

1. Choose the search from the data: an unsorted array needs linear search, O(n); a sorted array allows binary search, O(log n); many repeated lookups justify a hash map (O(1) average) or a balanced BST (O(log n), keeps order); graphs need BFS or DFS.
2. Implement binary search with a clear invariant: lo = 0, hi = n - 1, while lo <= hi, mid = lo + (hi - lo) / 2 (avoids int overflow in Java and C#), move lo or hi past mid, return -1 when not found.
3. Know the common variants: first or last occurrence among duplicates (lower and upper bound), insert position, a rotated sorted array, and binary search on the answer space.
4. Test the off-by-one edges: empty array, one element, target below or above every value, duplicates. A half-open range (hi = n, while lo < hi) also works, as long as the update rules match it.
5. Know the library calls: Python's bisect.bisect_left; Java's Arrays.binarySearch returns -(insertion point) - 1 when the key is missing; C#'s Array.BinarySearch returns the bitwise complement of the next larger element's index.

Follow-up

• Return the first index of the target in a sorted array that contains duplicates.
• Search for a target in a sorted array that has been rotated.
• When does BFS give the shortest path, and when does it not?

Solve a coding challenge related to sorting algorithms and explain your choice of algorithm.

Medium · Coding / Algorithms

Approach

1. Clarify the input before choosing: size, key type, whether the sort must be stable, the memory limit, whether the data is nearly sorted, and the range of the values.
2. Compare the standard algorithms on the properties that decide: merge sort is O(n log n) worst case, stable, O(n) extra memory; quicksort is O(n log n) on average, O(n^2) worst case, in place, not stable; heap sort is O(n log n) worst case, O(1) extra, not stable; insertion sort is O(n^2) but close to O(n) on nearly sorted or tiny inputs; counting or radix sort is O(n + k) for integer keys in a small range.
3. Know what the built-ins do: Python's sorted and list.sort use Timsort (stable, adaptive); Java's Arrays.sort uses dual-pivot quicksort for primitives and TimSort (stable) for objects; .NET's Array.Sort uses introsort and is not stable.
4. If asked to write one, do merge sort or quicksort cleanly. For quicksort, pick a random pivot so sorted input does not hit the O(n^2) worst case.
5. Recognise sorting-based problems: merge overlapping intervals, meeting rooms, sorting with a custom comparator, k-th largest via quickselect (O(n) average) or a size-k heap (O(n log k)).

Follow-up

• Why does stability matter when you sort by one key and then by another?
• The data does not fit in memory. How do you sort it?
• Find the k-th largest element without sorting the whole array.

Discuss the time complexity of your solution and how you could optimize it.

Medium · Coding / Algorithms

Approach

1. Derive the cost from the code, not from memory: nested loops over n give O(n^2); halving the range each step gives O(log n); a sort costs O(n log n); for recursion, write the recurrence (T(n) = 2T(n/2) + O(n) solves to O(n log n)). Always state space too, including the recursion stack.
2. Name the bottleneck, then the matching technique: a hash map or set instead of an inner lookup loop (two-sum drops from O(n^2) to O(n)), sorting plus two pointers, prefix sums for range queries, a sliding window, memoization for overlapping subproblems, a heap for top-k.
3. Be precise about the kind of bound: hash lookups are O(1) on average but O(n) in the worst case; appending to a dynamic array is amortized O(1); quicksort's average case is not its worst case.
4. Point out hidden costs in your language: repeated string concatenation in a loop is quadratic in Java (use StringBuilder); 'x in list' in Python is O(n) while 'x in set' is O(1) on average; slicing copies.
5. Know when to stop: reading the input is already Omega(n), and comparison sorting is Omega(n log n), so a solution at that bound can only improve by constant factors or memory.

Follow-up

• What is the space complexity, counting the recursion stack?
• Can you solve it with O(1) extra space, and what does that cost in time?
• How does the running time change if the input is already sorted?

Provide an example of a project where you applied algorithm design to solve a problem.

Medium · Coding / Algorithms

Approach

1. Pick a real case where the choice of algorithm or data structure changed the outcome: deduplicating or matching records with a hash index, resolving task dependencies with a topological sort, an LRU cache, interval merging or batching.
2. Open with the naive version, its complexity and why it failed at real data size (for example, pairwise comparison is O(n^2) and does not finish on a few hundred thousand records).
3. Describe your design with its complexity and correctness edges: the key you normalised before hashing, how you handled ties, nulls and duplicates, and the extra memory or preprocessing it cost.
4. Show evidence: runtime and memory before and after, and how you proved the results were unchanged (unit tests on edge cases, diffing the output against the brute-force version on samples).
5. Close with the trade-off you accepted (approximation, memory, index maintenance) and what you would change now.

Follow-up

• How did you show the faster version gave the same results as the slow one?
• What happens when the data grows by another order of magnitude?
• Which simpler approach did you consider, and why did you reject it?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

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

Approach

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

Follow-up

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

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

• A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
• Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

What programming languages are you proficient in, and how have you applied them in past projects?

Medium · Technical / Domain Questions

Approach

1. Lead with one or two languages you can defend in depth, ideally from the ones the role requirements candidates describe list (Java, C#, Python). For each, name one project, what you built in it and why that language fit the job (a typed, long-lived service on the JVM or .NET versus Python for scripting or data work).
2. Prove depth with a language-specific fact you actually ran into: Java generics are erased at runtime, C# async methods return a Task you must await, CPython's GIL stops threads running Python bytecode in parallel so CPU-bound work went to multiprocessing.
3. Name the toolchain around the language, since it shows you shipped: build tool (Maven or Gradle, dotnet CLI, pip or Poetry), test framework (JUnit, xUnit or NUnit, pytest) and how the code reached production.
4. Grade yourself honestly: separate languages you have shipped production code in from ones you have only read or patched. If React or Angular is on your CV, have one component-state or data-fetching bug ready to explain.

Follow-up

• Why did you choose that language over the alternative for that project, and would you choose it again?
• How does garbage collection in your main language affect latency, and have you ever had to tune it or work around it?
• How do you handle errors in that language: exceptions, checked exceptions, or explicit result values, and why?

Explain the concept of RESTful APIs and how you’ve implemented them.

Medium · Technical / Domain Questions

Approach

1. Define REST by its constraints: resources named by URIs, a uniform interface of HTTP methods, stateless requests that each carry their own auth and context, cacheable responses, and a layered client-server architecture, with JSON as the usual representation.
2. Get method semantics right: GET is safe and idempotent; PUT replaces a resource and is idempotent; DELETE is idempotent; POST creates or triggers and is not idempotent; PATCH applies a partial change and is not guaranteed idempotent. Match status codes: 201 with a Location header, 204, 400, 401 (not authenticated) versus 403 (authenticated but not allowed), 404, 409, 429.
3. Cover the design details: plural resource nouns with nesting (/orders/{id}/items), filtering and pagination through query parameters (cursor pagination stays stable under inserts, offset does not), a versioning scheme (/v1 path or header), and one consistent error body.
4. Describe your own implementation concretely: the framework (Spring Boot, ASP.NET Core, Flask, FastAPI or Django REST Framework), input validation, OAuth2 bearer tokens or JWT for auth, an OpenAPI spec, and how you tested the endpoints.
5. Name the trade-offs: idempotency keys so a retried POST does not double-create, ETag with If-Match for optimistic concurrency, and when GraphQL or gRPC would suit better than REST.

Follow-up

• How would you version this API without breaking existing clients?
• A client's POST times out and it retries. How do you make sure the record is not created twice?
• Two users edit the same resource at once. How does your API stop the second write from silently overwriting the first?

Discuss a challenging technical problem you faced and how you resolved it.

Medium · Technical / Domain Questions

Approach

1. Choose a problem with a real technical crux you can explain in depth (a race condition, a slow query, a memory leak, a failing integration), not a scheduling or people problem.
2. State what made it hard: data volume, no allowed downtime, a legacy system with no tests, or a bug you could not reproduce on demand.
3. Walk the diagnosis in order: each hypothesis, the evidence you gathered, and what that evidence ruled out.
4. Lay out the options you weighed and why you picked one, with its cost (for a slow query: add an index versus rewrite the query versus cache the result, and what each does to writes and freshness).
5. End with a measured result (latency, error rate or runtime before and after), keep 'I' distinct from the team's work, and say what you would do differently.

Follow-up

• Which alternative did you reject, and what would have made it the right choice?
• How did you confirm the fix worked and did not break anything else?
• If the problem came back, what alert or metric would show it first?

How would you redesign a feature that users find confusing?

Medium · Problem-Solving / Case Studies

Approach

1. Locate the confusion with evidence before redesigning anything: support tickets, funnel or step drop-off, validation-error rates, task completion time, session recordings and a few short user interviews.
2. Name the root cause, because each one has a different fix: a mismatch with the user's mental model, too many options at once, vague labels, hidden state, or error messages that do not say how to recover.
3. Match the fix to the cause: sensible defaults, progressive disclosure, fewer steps, inline validation with specific messages, visible status, plus accessibility basics (labelled inputs, keyboard navigation, sufficient contrast).
4. Ship it safely: put the new flow behind a feature flag, run an A/B test or staged rollout against one success metric (completion rate, tickets per week) and a guardrail metric, and keep the old flow available for rollback.
5. Cover the engineering side: refactoring components and state in the front end (React or Angular), and keeping any backend contract change backward compatible while both flows are live.

Follow-up

• How would you know the redesign removed the confusion rather than moving it to another step?
• Some users rely on the old behaviour. How do you handle them?
• With limited time, which confusing part would you fix first, and why?

Discuss a project where you had to pivot due to unexpected challenges. What was your approach?

Medium · Problem-Solving / Case Studies

Approach

1. Pick a project where a technical discovery forced the change: a vendor API missing a feature it needed, performance that collapsed at real data volume, or a requirement that changed underneath the design.
2. Say what the signal was and when you saw it (a benchmark, a prototype, a failed load test) and how early in the project that was.
3. Show the decision: the options (push through, change approach, cut scope), the criteria (switching cost, schedule, risk), who you consulted and how you told stakeholders about the new plan.
4. Describe the execution: what work you salvaged, how you kept scope contained and how you cut over without breaking what was already live.
5. Finish with the outcome and the lesson, for example testing the riskiest assumption with a short prototype before committing to the full build.

Follow-up

• Which earlier signal did you miss, and how would you catch it sooner next time?
• How did you explain the change of plan to people who had already planned around the original one?
• What did the change cost, and was it worth it?

Given a specific scenario, how would you ensure the scalability of the solution?

Medium · Problem-Solving / Case Studies

Approach

1. Clarify the load before designing: users and requests per second (peak, not just average), data size and growth rate, read/write ratio, latency target and how fresh the data must be.
2. Measure to find the actual bottleneck, then make the application tier stateless behind a load balancer so it scales horizontally, with sessions moved out of process memory into a shared store or tokens.
3. Scale the data tier in order of cost: indexes and query fixes, connection pooling, read replicas for read-heavy traffic (accepting replication lag), a cache-aside layer with TTLs and explicit invalidation, then partitioning or sharding once one primary cannot take the writes.
4. Move slow work off the request path: a queue for reports, notifications and batch jobs, idempotent consumers so retries are safe, rate limiting and backpressure so a spike degrades service instead of taking it down.
5. Validate it: autoscaling and managed databases on a cloud such as AWS or Azure, a CDN for static assets, and a load test that tracks p95 and p99 latency rather than the average.

Follow-up

• Reads grow tenfold but writes stay flat. What changes in your design?
• How do you keep the cache consistent with the database when a record is updated?
• What would you load test, and what result tells you the system is ready?

Describe your thought process when analyzing a system’s performance issues.

Medium · Problem-Solving / Case Studies

Approach

1. Pin down the symptom: which endpoint or job, which percentile is bad (p50 or p99), throughput, and since when; then line it up against deploys, traffic changes and data growth.
2. Check resources with the USE method (utilization, saturation, errors) and services with RED (rate, errors, duration) to decide whether the time goes on CPU, memory or GC, disk I/O, network, or waiting on a dependency.
3. Drill down with the right tool: distributed tracing to find the slow span, a profiler or flame graph for CPU hot paths, and for the database the slow query log, EXPLAIN ANALYZE, missing indexes, N+1 queries from the ORM, lock contention and connection pool exhaustion.
4. Fix the largest contributor first and measure before and after under the same load. By Amdahl's law, speeding up a step that takes 10% of request time can save at most 10%.
5. Prevent recurrence: alert on p99 latency, set a performance budget, and add a load or benchmark test that would have caught the regression.

Follow-up

• Average latency looks fine but p99 is bad. What do you suspect first?
• How would you find an N+1 query problem in an ORM-based service?
• CPU usage is low but requests are slow. Where do you look?

How would you approach integrating a new technology into an existing system?

Medium · Problem-Solving / Case Studies

Approach

1. Start from the problem, not the tool: what the current system cannot do, how success will be measured, and how the new technology compares with alternatives, including changing nothing. Check licensing, support, security review, team skills and compliance (for health data, whether a vendor will sign a HIPAA business associate agreement).
2. Prove it with a time-boxed prototype on realistic data and judge it against the success criteria you set beforehand.
3. Integrate behind an interface or adapter so the rest of the system does not depend on the new component directly, and replace old pieces gradually (the strangler fig pattern) instead of rewriting everything at once.
4. Roll out in stages: feature flags, shadow traffic or dual writes with output comparison, a canary, a tested rollback path and a data migration plan.
5. Make it operable before calling it done: monitoring and alerts, a runbook, documentation, a named owner and training for the people who will support it.

Follow-up

• How would you migrate existing data into the new system without downtime?
• What result would make you abandon the integration halfway through?
• How would you persuade a team that is happy with the current stack?

How do you approach debugging and troubleshooting code?

Medium · Technical / Domain Questions

Approach

1. Reproduce first: pin down the exact input, environment, version and config, then write a failing test that captures the bug so the fix can be verified.
2. Read the evidence before theorising: stack trace, logs, recent changes. Then halve the search space each step: git bisect across commits, or bisect the input and config until one variable flips the result.
3. Pick the tool for the failure type: a debugger with conditional breakpoints locally, structured logs with a request or correlation id across services, metrics for resource problems. For intermittent bugs, suspect concurrency, timeouts, caching, time zones and data-dependent branches.
4. Fix the root cause, not the symptom: ask why the bad value got there (an unchecked null from an upstream call, missing validation), keep the regression test, and search for the same pattern elsewhere.
5. Close the loop: confirm the fix where it originally failed, watch error rates after deploy, and write a short post-incident note if it reached users.

Follow-up

• How do you debug something that only happens in production, where you cannot attach a debugger?
• Walk me through a real bug: what was your first wrong hypothesis and what ruled it out?
• How would you track down a flaky test that fails one run in twenty?

Suggested practice checklist

The plan follows the reported loop: background and stack first for the phone screen, then the REST, debugging, case-style and coding questions, then behavioral stories and a full mock.

Day 1 — Map the loop and inventory your stack

• Write the two reported stages and the four question groups (experience, case-style, coding, behavioral) on one page, and mark your weakest group.
• For each of Java, C#, Python, SQL and NoSQL that you really know, write one project, one decision and one thing you would change.

Deliverable: A one-page loop map plus a skills inventory with a project for each language and data store.

Day 2 — Phone screen: background and a hard problem

• Write and say aloud a short career summary that links your projects to the stack the role lists.
• Outline your answer to the challenging technical problem question: constraint, options, decision, result.
• Write your reason for wanting healthcare software, and two questions for the team.

Deliverable: A spoken career summary, one problem story outline and two questions to ask.

Day 3 — Debugging, REST and SQL fluency

• Write your debugging sequence and apply it to a real bug from your history.
• Design one REST resource on paper: URLs, verbs, status codes, an idempotent update and an error format.

Deliverable: A debugging checklist, a one-resource REST table and two solved SQL practice questions.

Day 4 — Coding and algorithm questions

• Implement reversing a linked list both iteratively and recursively in your strongest language and test the short-list cases.
• Implement a linear search and a binary search, stating when each applies.
• Compare insertion, merge and quick sort on stability, memory and worst case, then code one and state its complexity.

Deliverable: Working code for list reversal, both searches and one sort, with a complexity table.

Day 5 — Case-style discussions

• Outline how you would redesign a confusing feature: find the confusion with user evidence, change it, then measure.
• Write a performance-analysis sequence (measure, find the bottleneck, change one thing, re-measure) and a scalability answer for a scenario you pick.
• Outline a plan for introducing a new technology into an existing system, including rollout and rollback.

Deliverable: Four one-page outlines: confusing feature, performance, scalability and new-technology integration.

Day 6 — Behavioral stories

• Write STAR outlines for a difficult colleague, receiving feedback, mentoring a junior engineer, prioritising several projects and a quick decision.
• Cut each outline to the actions you took and a concrete result, then say each one aloud.

Deliverable: Five rehearsed STAR stories, each with a named personal action and a result.

Day 7 — Full mock

• Run a phone-screen style mock: career summary, languages and database experience.
• Run an in-person style mock: code a linked-list reversal, explain a debugging case and tell one behavioral story, ideally to someone who is not an engineer.
• Record yourself, and write a fix list for the three weakest answers.

Deliverable: A recording or notes from both mocks and a three-item fix list.

Stories for the team and fit conversations

Candidates report that cultural fit can matter as much as technical skill in some interviews, and the reported prompts cover difficult colleagues, feedback, mentoring, prioritisation and quick decisions. Prepare stories where you took the action, and keep the technical parts short enough to explain to a non-engineer.

1. What does community service mean to you, and how do you see it reflected in your professional life?

Can you describe your experience with database management systems?

Medium · Technical / Domain Questions

Approach

1. Open with an inventory, then go deep on one system: name the databases you have actually used (for example one relational and one document or key-value store), what data they held, and whether you designed the schema or consumed one someone else built. Be explicit about the line between operating a database and using it through an ORM.
2. Pick one decision you owned and tell it as a story: the situation, the access pattern you had to support, the choice you made (an index, a table split, a denormalised column, SQL versus NoSQL) and the measured result, such as a query plan before and after.
3. Cover the unglamorous parts that show real experience: a schema migration on a table in use, transactions and isolation where two writers collided, backups or restores, and how access to sensitive records was restricted.
4. Say what you would do differently with hindsight. The common mistake is a list of product names with no decision, number or failure behind them.
5. Match the examples to the stack listed for this role: SQL and NoSQL are both named, so be ready to state when you would reach for each.

Follow-up

• How did you decide which columns to index, and what did the index cost on writes?
• When would you choose a document store over a relational one, and what do you give up?
• How did you run a schema change on a table that was being used without breaking readers?

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

Medium · Behavioral / Leadership

Approach

1. Choose a conflict about specific behaviour (missed review turnarounds, changes merged without discussion, dismissing suggestions), not a personality. Describe the behaviour and its effect on the work in two sentences, without name-calling.
2. Show the order of your actions: you first tried to understand their constraints in a private, direct conversation, then agreed a concrete working arrangement (review windows, a written interface, a shared checklist), and only then involved a manager if that failed.
3. Include your own contribution to the friction. Naming one thing you changed makes the story credible and shows self-awareness.
4. Close with the result: what shipped, how the working relationship changed, and what you now do earlier in a new team. A story with no resolution, or with you as the hero and them as the villain, is the one that sinks this answer.

Follow-up

• At what point did you involve your manager, and what did you ask them to do?
• What would you have done if the conversation had changed nothing?
• What did you learn about your own communication from that situation?

What is your approach to receiving and implementing feedback?

Medium · Behavioral / Leadership

Approach

1. Give a real example of feedback that stung or changed your mind, such as a code-review comment about structure, a manager's comment on communication or a post-incident finding. Generic claims like "I welcome feedback" with no example are the failure mode.
2. Describe how you received it: you restated it to confirm you understood, asked for a concrete example, and separated what was fact from what was preference.
3. Show the implementation: the specific habit or code change that followed (smaller pull requests, tests before review, earlier design check-ins) and the evidence it worked, such as fewer review round-trips.
4. Include one case where you disagreed and pushed back with evidence rather than accepting or ignoring it. Since code review is among the listed responsibilities, add how you give feedback to peers: specific, about the code, with a suggested alternative.

Follow-up

• Tell me about feedback you disagreed with. What did you do?
• How do you give a peer feedback on a pull request you think is wrong?
• How did you check that you had actually changed the behaviour?

Can you give an example of how you have mentored or supported a junior engineer?

Medium · Behavioral / Leadership

Approach

1. Describe the junior engineer's starting point and the goal in concrete terms (first production change, learning the codebase, getting comfortable with debugging), then the plan you set, not just that you were available.
2. Name the methods: pairing on the first tasks, scoping work into pieces they could finish, review comments that explain the reason behind a change, and handing over ownership of a small feature so they made decisions themselves.
3. Show restraint. The weak version of this story is one where you took over the hard parts; explain where you let them struggle productively and where you stepped in.
4. Give the outcome in their growth (they shipped a feature, handled an on-call issue, began reviewing others' code), and say what it cost you in time and how you protected your own deliverables.

Follow-up

• What did you do when they did not follow your advice?
• How did you balance the mentoring with your own deadlines?
• How do you word review comments on a junior engineer's pull request?

How do you prioritize tasks when managing multiple projects?

Medium · Behavioral / Leadership

Approach

1. Describe your method before the example: list everything committed, rank by impact and deadline, note dependencies and who is waiting, and then re-check the order as things change rather than once a week.
2. Use a real situation with two competing projects or requests: what you ranked first and why, what you deferred, and how you told the affected people. The communication step is the part most answers omit.
3. Show that you negotiate scope rather than only work longer hours: you shortened a deliverable, split a release or asked a product manager to choose between two items.
4. Cover interruptions such as a production bug: how you decide whether it pre-empts planned work, and how you record the trade-off so it is visible.

Follow-up

• Two product managers each say their request is urgent. How do you decide?
• Tell me about something you decided not to do and how you communicated it.
• How do you handle a production issue that lands mid-sprint?

Describe a situation where you had to make a quick decision under pressure.

Medium · Behavioral / Leadership

Approach

1. Choose an incident with a real time constraint, such as a failing release, a data problem or a blocked launch. State what you knew, what you did not know, and why waiting was costly.
2. Walk through the options you saw and the reasoning for the one you took. Favouring a reversible action (rollback, feature flag, narrow fix) over an irreversible one is a strong reasoning pattern if it is true of your story.
3. Show communication under pressure: who you told, what you asked them to do, and how you kept stakeholders informed while it was unresolved.
4. Finish with the outcome and the follow-up: a review of the decision, what you changed so the decision would not need to be rushed again, and one thing you would do differently. Stories of panic with no reasoning, or where you simply got lucky, are the ones to avoid.

Follow-up

• What information were you missing, and how did you allow for that?
• Would you decide differently if you had more time?
• What process change came out of it?