Avanos Software Engineer Interview Guide

The 60-second brief

Avanos makes medical devices. Candidates describe Software Engineer openings in three areas, R&D, Quality Systems (QMS) and Manufacturing Engineering, with day-to-day work alongside hardware engineering, regulatory affairs and product management. The reported responsibilities cover the whole life of a software component: turning product requirements into working code, integrating that code with physical devices together with hardware and systems engineers, testing it, keeping documentation complete enough for compliance, and improving existing systems for performance and safety. The work therefore sits inside a regulated environment, and the reported interview questions are about exactly that: getting software right when a device, a quality record or a production line depends on it.

This guide is for software engineers interviewing for an Avanos Software Engineer opening, whether the team sits in R&D, Quality Systems or Manufacturing Engineering. It covers the two reported stages, Initial Screening and Technical Discussions. It gives answer outlines for the ten questions candidates report, grouped as Technical and Domain Expertise and Problem-Solving and Methodology, and lists the regulated-software vocabulary worth knowing: risk classes, traceability, verification versus validation, change control. It also covers the behavioral stories to have ready and a seven-day plan. If you have never worked in a regulated industry, this guide shows you how to frame your existing experience in those terms.

Candidates report two rounds over roughly 2-4 weeks: an Initial Screening that reviews your background for fit, then Technical Discussions that go deep on practical engineering with members of the team.

Focus areas

• Molding Engineering
• Quality Systems (QMS)
• Behavioral Interviewing

Overview

The Avanos Software Engineer role, as candidates describe it, is software for medical devices and the systems around them. It can mean R&D work on device software, Quality Systems tooling, or software supporting Manufacturing Engineering. Across all three, candidates list the same responsibilities: implement requirements, integrate with hardware, test thoroughly, keep compliance documentation in order, and improve systems already in service. The requirements candidates describe are proficiency in the team's core programming languages, experience with software development lifecycles and a record of shipping reliable code. Experience in medical device manufacturing or another regulated industry, familiarity with QMS procedures and cross-functional project work are listed as nice-to-haves.

The ten reported questions are open-ended and about process. None is an algorithm puzzle. Five sit under Technical and Domain Expertise: making code meet safety and quality standards, troubleshooting a production failure, documentation for regulated software, balancing speed against regulation, and running code reviews. Five sit under Problem-Solving and Methodology: prioritising technical debt, designing a modular and durable system, changing course under unexpected constraints, validating against end-user requirements, and choosing metrics for a new feature. Candidates report scenarios modelled on real engineering work rather than theoretical puzzles. A strong answer is a concrete method backed by a story from your own work, not a list of principles.

That shapes the preparation. Learn the vocabulary of regulated software well enough to use it accurately: risk-based rigor, requirement-to-test traceability, verification versus validation, change control, third-party (SOUP) components, CAPA. Then map your own projects onto it, even if they come from another industry. The topic areas candidates list are Molding Engineering, Quality Systems (QMS), Behavioral Interviewing, Communication Skills and Manufacturing Process Engineering. If your opening faces manufacturing or quality, be ready to discuss software that touches production equipment, process data or quality records, and how you would validate such software before it is used.

Collaboration is reported as an assessed area too. That means explaining technical risk to project managers and handling disagreements about technical direction with quality, regulatory and manufacturing colleagues. Prepare at least two stories in which you changed a decision by making a risk understandable to someone without your technical background.

The Avanos interview process

Candidates report two stages, a screening conversation followed by technical deep-dive discussions. They do not report which reported question comes up in which stage, so prepare every question for the technical stage.

Initial Screening

Candidates describe the Initial Screening as a high-level look at your experience to judge whether it fits the role. Treat it as the place to establish two things. First, which of the three areas the opening belongs to (R&D, Quality Systems or Manufacturing Engineering), because that decides which of your projects to lead with in the technical stage. Second, a short, specific account of your background that already uses the language of reliability, testing and regulated work. Candidates report that most R&D and Quality Systems roles are based in the Alpharetta, GA office, so confirm location and on-site expectations here rather than late in the process. Close by asking how the technical discussions are organised and who you will speak with.

What to demonstrate

• How well your background matches the opening's area: R&D, Quality Systems or Manufacturing Engineering
• Relevant exposure to software development lifecycles, testing and any regulated or safety-relevant work
• How clearly you explain your own experience and what you personally owned

How to prepare

• Write a two-minute walkthrough of your background that leads with one project where safety, quality or regulatory constraints shaped a technical decision, and say what you decided.
• Prepare one sentence on each of the three areas (device R&D, quality systems, manufacturing software) saying how your experience transfers, so you can adapt once you learn which team the opening sits in.
• List your questions: team area, the languages and tools the team uses, location expectations, and how the technical discussions are structured.

Technical Discussions

Candidates describe this stage as in-depth technical conversations with members of the team, centred on practical engineering rather than puzzles. The reported question set suggests what the conversation covers: how you keep code safe and compliant, how you document, review, design, validate and measure software, and how you behaved during real incidents and changes of plan. Approach each question with a method first (the steps you follow and why), then one concrete example from your own work with a result. Expect to defend design choices and trade-offs from your own projects. For each project you mention, know the constraints, the alternatives you rejected and what you would change now.

What to demonstrate

• Depth on your own projects: design choices, trade-offs and what you would do differently
• Engineering practice in a regulated setting: testing, traceability, documentation, code review and change control
• Practical judgment on system design, validation and feature metrics
• How you communicate technical risk and resolve disagreements with cross-functional partners

How to prepare

• Pick one project and prepare a ten-minute deep dive: architecture sketch, the requirement it served, how it was tested, one failure you handled and one trade-off you would revisit.
• Rehearse all ten reported questions out loud, each as a method followed by a personal example. Candidates do not report which stage each one comes from.
• Revise testing frameworks and CI/CD as they apply in a regulated environment: how a pipeline produces test evidence, enforces review and ties a change to a requirement.
• Learn the core terms (risk classes, design controls, verification versus validation, SOUP, CAPA, change control) well enough to use them correctly, without overclaiming experience you lack.

Where candidates lose points

Answering the safety and compliance questions with "I write unit tests and get code reviewed", with no risk basis and no traceability.

Structure the answer as risk classification, then requirement-to-test traceability, then automated gates, then control of change and third-party code. Even without medical device experience, show how rigor scaled with risk in your past work and how you could prove a requirement was tested.

Describing documentation as something written at the end of a project to satisfy an audit.

Present documentation as part of each change: requirements, design notes and tests updated and reviewed with the code, and traceability generated from test tags. Have one example where good records shortened an investigation or an audit.

Answering the speed-versus-regulation question in a way that suggests skipping or postponing verification under deadline pressure.

State plainly that required verification is not negotiable, then explain where speed actually comes from: risk-based scoping, architectural separation, small increments and automated evidence. Say how you would raise a deadline conflict with the people who own the decision.

Behavioral stories (production failure, technical debt, change of course) told as "we", with no personal decision and no measured outcome.

For each story, write down the one decision you made, the alternative you rejected and a number for the result before the interview. Rehearse saying "I" for your actions and naming others' roles accurately.

Answering the feature-metrics question with averages and usage counts only.

Name a primary outcome metric tied to the feature's purpose, guardrails, latency percentiles, error and defect signals, a baseline measured before launch, and a rollback threshold decided in advance.

Questions & practice

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

• A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
• Where does the fingerprint get computed relative to request decompression and the body-size limit?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

1. One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
2. Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
3. Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
4. If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

• The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
• How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Merge partitioned event streams into one ordered feed with bounded lateness

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

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

Approach

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

Follow-up

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

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

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?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

• The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
• How do you verify the backfill actually finished, given rows are still being inserted while it runs?

How do you ensure your code complies with rigorous safety and quality standards?

Medium · Technical and Domain Expertise

Approach

1. Start from risk, not from tools. I classify the software by the harm a failure could cause (in medical device terms, IEC 62304 safety classes A, B and C, backed by an ISO 14971 hazard analysis), and the class decides how much rigor each component gets.
2. Make every requirement traceable. Each requirement has an ID that links to its design element, the code that implements it and the test that verifies it. A requirement with no passing test, or code with no requirement behind it, is a gap I can find mechanically.
3. Automate the gates in CI: a coding standard enforced by linters or static analysis (MISRA C for embedded C, for example), unit and integration tests with coverage targets that scale with risk class, and mandatory independent review before merge. The pipeline output then serves as the evidence.
4. Control third-party code and change. List every external library (SOUP) with its version and known anomalies, and run an impact analysis on every change to decide which regression tests must run again.
5. Name the limits: coverage shows code ran, not that it is correct. So for the highest-risk paths I add boundary tests, fault-injection tests and a review of the error-handling paths specifically.

Follow-up

• An open-source library you depend on has a known bug in a safety-relevant path. What do you do?
• What coverage target would you set, and why is a coverage number alone not enough?
• How would you show an auditor that a specific requirement was tested on the released build?

What is your approach to documentation when working on regulated medical device software?

Medium · Technical and Domain Expertise

Approach

1. List the artifacts and what each is for: a software development plan, software requirements, architecture and detailed design, the risk management file, verification protocols and reports, a traceability matrix, a SOUP list, and release notes with known anomalies. Scale the depth to the software's risk class.
2. Treat documentation as part of the change, not a separate phase. I update requirements, design notes and tests in the same change as the code, so review covers all of them together and the record cannot drift from the code.
3. Keep documents under version control next to the code where you can. Tag tests with requirement IDs and generate the traceability matrix from those tags instead of maintaining it by hand.
4. Know the record-keeping basics: approvals and reviews need an audit trail with attributable sign-off (FDA 21 CFR Part 11 covers electronic records and signatures), and released documentation must match the released build exactly.
5. Close with an example: a time thorough documentation saved effort (an audit, an investigation, onboarding) or a gap you found and fixed.

Follow-up

• How do you keep the traceability matrix accurate when requirements change in the middle of a sprint?
• What belongs in a list of known anomalies at release, and who decides an anomaly is acceptable?
• How would you document a third-party library you did not write?

How do you balance the need for rapid development with the requirements of a strictly regulated industry?

Medium · Technical and Domain Expertise

Approach

1. State the principle first: speed comes from automation and scoping, never from skipping verification. I would not trade away a required verification activity for a date, and I say so plainly.
2. Use risk to decide where rigor goes. If the architecture separates safety-relevant components from lower-risk ones, and the separation is justified, the lower-risk parts can iterate faster. The high-risk core changes less often and with more evidence.
3. Work iteratively within design controls. Short increments, each with requirements, design, tests and documentation updated together, keep compliance work small and continuous instead of one large effort at release (AAMI TIR45 describes agile practice for medical device software).
4. Exploratory prototypes stay clearly outside the released product. Anything that ships is rebuilt or brought under controls with its requirements and tests, not promoted as is.
5. Give a personal example where automation (generated test reports, CI-enforced review, scripted traceability) made a release faster without dropping a required step.

Follow-up

• The deadline can only be met by skipping one verification activity. What do you do and who do you tell?
• How would you justify that a component is low risk enough to move faster?
• A requirement changes two weeks before release. Walk through your impact analysis.

How do you approach designing a system that must be both modular and highly durable?

Medium · Problem-Solving and Methodology

Approach

1. Clarify what 'durable' means here: long-lived and maintainable, resilient to failure, keeping data safe, or all three. Then state the requirements and the failure modes that matter before drawing anything.
2. Modularity: layers with explicit contracts, for example a hardware or device abstraction layer, domain logic, persistence and interface. Dependencies point inward (dependency inversion) so modules can be tested with fakes and replaced independently. Interfaces between separately released modules are versioned.
3. Durability: transactional writes or a write-ahead log so a power loss mid-write leaves a consistent state; checksums or CRCs on stored and transmitted data; idempotent operations with retries and backoff; watchdogs and health checks; and a defined fail-safe state when a dependency fails.
4. Trade-offs: each module boundary adds an interface to specify and verify, and extra layers can cost latency. In a regulated product, a clean boundary can also let a safety-relevant part be isolated and verified to a higher standard.
5. Walk through edge cases explicitly: power loss during a write, one module crashing while others run, version mismatch after a partial update, storage filling up.

Follow-up

• The device loses power halfway through writing a record. What state is the system in when it restarts?
• How do you version an interface between two modules that ship on different schedules?
• How does your module structure change the amount of verification you have to do?

How do you validate that your software solution meets the end-user requirements effectively?

Medium · Problem-Solving and Methodology

Approach

1. Separate the two checks. Verification asks whether I built what the specification says (tests against design inputs). Validation asks whether the specification meets the user's actual need in their actual setting. Passing the first does not guarantee the second.
2. Begin with testable user needs. I turn vague needs into measurable acceptance criteria ("an operator completes the batch entry in under N steps with no errors") and trace each one to a test.
3. Validate with real users in realistic conditions: simulated-use or usability sessions with representative users (IEC 62366 covers usability for medical devices), user acceptance testing for internal tools, and production-like data rather than clean test fixtures.
4. For software used in production or quality processes, validate for the intended use and scale the effort to risk. FDA's computer software assurance guidance applies this risk-based approach to such tools.
5. After release, close the loop: watch support tickets, complaints and usage data, and treat a recurring workaround by users as a validation failure to feed back into requirements.

Follow-up

• All tests pass but users still report that the feature does not work for them. What do you do?
• Turn "the screen should be easy to use" into a testable acceptance criterion.
• Two stakeholder groups give conflicting requirements. How do you decide what to validate against?

What metrics do you prioritize when evaluating the performance of a new software feature?

Medium · Problem-Solving and Methodology

Approach

1. Start from the purpose of the feature, then pick one primary metric that shows it is doing that job (task completion rate, time on task, error rate in the workflow it supports) and a small set of guardrails it must not harm.
2. Performance and reliability: latency as percentiles (p50, p95, p99), not averages, because a long tail hides behind a good mean. Add throughput, resource use (CPU, memory, storage growth), error and crash rates, availability, and time to recover.
3. Quality signals: escaped defects, support tickets and complaints linked to the feature, and rollback or hotfix frequency.
4. Method: measure a baseline before release, set thresholds in advance, instrument before launch, and decide ahead of time which guardrail breach triggers a rollback. Where possible, compare with a control group or a staged rollout rather than a before-and-after that can be confounded.
5. For manufacturing- or quality-facing software, also tie the feature to an operational outcome its users care about, such as fewer manual entry errors, while noting that other changes on the line can move the same numbers.

Follow-up

• Average latency looks fine but users say the feature is slow. What do you check?
• Which single metric would make you roll the feature back, and at what threshold?
• Usage is high but the outcome metric has not moved. How do you interpret that?

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

Medium · lock contention · ddl · migrations

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

Approach

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

Follow-up

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

Suggested practice checklist

Each day of the plan prepares one part of the reported loop: the screening pitch, the compliance and documentation questions, the design and validation questions, behavioral stories, a hands-on refresh, and a full mock of both stages.

Day 1 — Screening pitch and project inventory

• Write a two-minute background walkthrough that opens with a project where safety, quality or regulatory constraints shaped a decision.
• List three projects you could deep-dive. For each, note the requirement it served, how it was tested, one failure and one trade-off.
• Write one sentence each on how your experience transfers to R&D, Quality Systems and Manufacturing Engineering openings, plus your questions about team area and location.

Deliverable: A rehearsed two-minute pitch, a three-project inventory with failures and trade-offs, and a list of screening questions.

Day 2 — Safety, quality and documentation

• Write a one-page glossary in your own words: IEC 62304 safety classes, ISO 14971 risk management, design controls, verification versus validation, SOUP, CAPA, change control.
• Take one feature you built and trace it on paper: requirement ID, design element, code, test, and the evidence each produced.
• Answer the safety-and-quality-standards question and the regulated-documentation question out loud, each as a method plus one example.

Deliverable: A glossary page, one complete requirement-to-test trace, and two spoken answers recorded and reviewed.

Day 3 — Speed versus regulation, and validation

• Outline your answer to the speed-versus-regulation question: the non-negotiable, then risk-based scoping, architectural separation, small increments, automated evidence, and one personal example.
• Rewrite three vague user needs from your past work as measurable acceptance criteria, and describe how you would validate each with real users.
• Answer both questions out loud and cut any sentence that does not describe a step you would actually take.

Deliverable: Two outlined and spoken answers, plus three rewritten acceptance criteria with validation methods.

Day 4 — Modular, durable design and feature metrics

• Sketch a layered design for a device-connected or production-data system: abstraction layer, domain logic, persistence, interface, plus the durability mechanisms (transactional writes, checksums, retries, watchdog, fail-safe state).
• Walk the sketch through four failures: power loss mid-write, one module crashing, version mismatch after a partial update, full storage.
• Choose a feature you shipped and define its primary metric, guardrails, latency percentiles, baseline and rollback threshold.

Deliverable: One design sketch with written failure-mode notes, and a one-page metric plan for a real feature.

Day 5 — Behavioral stories

• Write STAR outlines for the production-failure story, the technical-debt story and the change-of-course story, each with one personal decision and one number in the result.
• Write two collaboration stories: explaining a technical risk to a project manager or non-technical partner, and resolving a disagreement about technical direction.
• Tell each story out loud, then remove every "we" that hides what you did.

Deliverable: Five STAR outlines, each with a decision and a measured result, rehearsed aloud.

Day 6 — Hands-on refresh and code review

• Solve one practice coding exercise in the language your target team uses, writing tests for the edge cases before you finish.
• Work through one debugging and one SQL practice exercise, writing out your diagnosis sequence step by step.
• Review your own solution as if it were someone else's change, using the reliability checklist from the code-review question, then answer that question out loud with a defect you once caught.

Deliverable: A tested coding solution, two written exercise walkthroughs, a self-review against the checklist, and a spoken code-review answer.

Day 7 — Full mock of both stages

• Have a friend run a screening-style conversation using your pitch, then a technical discussion that mixes your project deep-dive with three reported questions picked at random.
• Write down each answer where you lacked a concrete example or used a term imprecisely, and fix those notes.
• Finalise your questions for the team, for example how the team handles verification evidence and change control in practice.

Deliverable: A completed two-part mock, a list of fixed weak answers, and final questions for the team.

Behavioural preparation

Several reported questions are behavioral: a production failure, technical debt, a change of course under constraints, and your code-review practice. Candidates also report collaboration as an assessed area, specifically explaining technical risk to project managers and resolving disagreements about technical direction with quality, regulatory and manufacturing partners. Each story should show a decision you made, the risk you weighed and a measurable result.

1. Tell me about a time you explained a technical risk to a project manager or a non-technical partner and it changed a decision.
2. Describe a disagreement over technical direction and how you resolved it while keeping the project on schedule.

Can you describe a time you had to troubleshoot a critical system failure in a production environment?

Medium · Technical and Domain Expertise

Approach

1. Choose an incident where you led the diagnosis. Open with impact in one or two sentences: what failed, who was affected, and how you found out (alert, customer report, line stoppage).
2. Show containment before diagnosis: rollback, feature flag, failover or a manual workaround to stop the harm, while preserving evidence (logs, dumps, the failing input) before anything was restarted.
3. Walk through the diagnosis as a sequence: build a timeline, diff what changed recently (code, config, data, environment), reproduce in a lower environment, then narrow down by bisecting or testing one hypothesis at a time. Say which hypothesis turned out wrong and how you ruled it out.
4. Separate the trigger from the root cause, then give the permanent fix: the regression test that would have caught it, the monitoring you added, and how the incident was recorded (postmortem, or in a regulated setting a deviation or CAPA record).
5. The mistake that sinks this answer is a story told as 'we' with no personal decision, or one that ends at 'restarted the service'. Say 'I' and finish with a measured result.

Follow-up

• How did you choose between rolling back and shipping a hotfix?
• What did you change so that this whole class of failure cannot recur?
• Who did you notify during the incident, and when?

Explain your process for conducting code reviews to ensure system reliability.

Medium · Technical and Domain Expertise

Approach

1. Describe the process as a checklist tied to reliability: does the change implement the linked requirement; are error paths, timeouts, null or out-of-range inputs and units handled; are there concurrency or resource-leak risks; do the tests exercise the change, including the failure cases?
2. Keep human review on logic by automating the rest: formatting, linting and static analysis run in CI before a reviewer looks. Small, single-purpose changes get reviewed properly; very large ones get split or walked through by the author.
3. Use independence and records: a reviewer other than the author, required approvals before merge, and the approved review kept as evidence of who checked what.
4. Give one real defect you caught in review: what it was, why tests missed it, and what you added (a test, a checklist item, a static-analysis rule) so the next one is caught automatically.
5. Mention tone: comments explain the risk and suggest a fix rather than giving orders, and when you are uncertain you ask a question instead of blocking.

Follow-up

• How do you review a 2,000-line change?
• Tell me about a defect that got through your review. What changed afterwards?
• How do you handle disagreement with an author who is more senior than you?

Describe a time you identified a significant technical debt issue and how you prioritized its resolution.

Medium · Problem-Solving and Methodology

Approach

1. Situation: the system and the symptom that exposed the debt, with evidence such as repeated incidents in one module, changes taking far longer than estimated, flaky tests, or a dependency no longer supported.
2. Show how you put a cost on it and set priority. I estimated impact (incident count, hours lost per change, risk to safety or compliance) against the cost of the fix, then ranked it against roadmap work by risk times frequency rather than by annoyance.
3. Action: an incremental plan, not a rewrite. Add characterization tests to pin current behaviour, refactor behind a stable interface (strangler pattern), and ship in slices you can verify. Say how you got product or project management to give it capacity.
4. Result in numbers: fewer incidents, shorter change lead time, or a removed compliance risk. Mention one debt you deliberately left alone and why.
5. What sinks this story: 'the code was messy so I rewrote it' with no measured cost, no stakeholder agreement and no protection against regressions.

Follow-up

• What technical debt did you decide not to fix, and why?
• How did you make sure the refactor did not change behaviour?
• How did you convince the product owner to give up roadmap time?

Tell me about a technical project where you had to pivot your strategy due to unexpected constraints.

Medium · Problem-Solving and Methodology

Approach

1. Situation and original plan: what you set out to build, the approach chosen, and why it was reasonable at the time.
2. The constraint that appeared: a hardware limitation, a regulatory or quality requirement found late, a vendor or library change, a budget or schedule cut. Be specific about when and how you found out.
3. Your decision: the options you considered, how you compared them (risk, cost, schedule, what work could be kept), who you consulted, and how you told stakeholders about the change in scope or date.
4. Result and lesson: what shipped, what the change cost, and what you now do earlier to catch such constraints (for example, involving quality or hardware partners at requirements time).
5. What sinks this story: describing the change of course as something that happened to the team, without a decision you made, or hiding that the original plan had a flaw you could have spotted.

Follow-up

• Looking back, could you have spotted the constraint earlier? How?
• What did you throw away, and what did you manage to keep?
• How did stakeholders react to the change, and how did you handle that?