Abby Care Software Engineer Interview Guide

The 60-second brief

Abby Care runs a care platform for family caregivers. It trains them, employs them and pays them for the care they give at home. Candidates describe the engineering work as building web products and APIs for that platform, modeling care workflows for patients at home or in hospital, and building AI-powered features on new systems. Engineers work alongside clinical, compliance, operations and product colleagues, and the role is described as based in San Francisco. For you, that means every technical answer sits on health data: who can see it, how it is stored, and what happens when a caregiver's connection drops.

This guide is for Software Engineer candidates preparing for Abby Care. It walks through the four reported rounds (Initial Screening, Coding Challenge, System Design Interview, Behavioral Assessment) and gives specific preparation for the reported question groups: domain modeling and APIs, coding and SQL, system design on AWS, and behavioral prompts about regulation, clinical partners and prioritization. The stack candidates describe is TypeScript, Python, AWS and general web and API development, and the listed requirements point to experienced engineers (5+ years) with product-lifecycle ownership.

Candidates report four rounds over roughly 3-5 weeks: an Initial Screening with the hiring manager, a TypeScript Coding Challenge, a System Design Interview and a Behavioral Assessment with a product manager.

Focus areas

• TypeScript
• Python
• System design

Overview

The reported questions fall into four groups. Domain modeling and APIs: a questionnaire list for a patient plus where the patient is (home or hospital), an endpoint for vital signs from in-home devices, offline-first data entry in low-connectivity areas, a validation pipeline for compliance reporting, and secure parsing and aggregation of patient care logs. Coding: a live TypeScript exercise on data manipulation and API integration, a caregiver scheduling-route algorithm, a slow patient-outcome SQL query, a rate-limiting middleware and a Python async debugging task. System design: urgent-update notifications, secure patient-records storage, a tenfold surge on AWS, monolith versus microservices for an early-stage platform, and real-time chat with clinical support. Behavioral: regulation versus speed, clinical and operations partnership, backlog prioritization, a technical disagreement, and why caregiving technology.

Reports do not tie individual questions to individual rounds, so prepare by question type. A coding stage is where you practise the coding and SQL questions; the system-design stage is where the architecture and modeling questions fit; the behavioral stage is the last one and is described as run by a product manager. Treat every question as possible in any technical stage and rehearse each one end to end.

Because the data is health data, candidates describe security and compliance as part of design answers rather than an add-on. Put encryption in transit and at rest, access control and audit logging into your first diagram, and say how a retry, an offline sync or a duplicate device reading is handled so nothing is lost or double-counted. The reported data-modeling prompt about a patient's questionnaires and location is open-ended, so spend the first minutes on states, owners and edge cases before drawing tables.

On the language side, the Coding Challenge is described as TypeScript, while the requirements candidates describe list both TypeScript and Python, plus AWS and API development. Be able to write typed, validated TypeScript for incoming JSON and fluent Python asyncio, and be ready to explain either one in a design discussion. Reports also mention full-stack work with front-end user-experience considerations, and list a scenario about setting up a secure CI/CD pipeline on AWS, so prepare a small typed UI state flow and an outline of that pipeline as well. The behavioral round is with a product manager and revolves around clinical, compliance and operations partners, so your stories should name those kinds of counterparts and the decisions you made with them.

The Abby Care interview process

Candidates report these four stages over about 3-5 weeks, and say the format can shift with the team and seniority, so confirm each stage with your recruiter.

Initial Screening

Candidates report that the opening stage is a call with the hiring manager. Come with a short account of the work you have done and a clear reason for applying. No technical exercise is reported for it. Treat it as the place to connect your experience to what the role lists: web and API work, TypeScript, Python, AWS and shipping products from concept to launch. The reported behavioral question about why you want to build caregiving technology at Abby Care is the natural one to rehearse here.

What to demonstrate

• How clearly you describe your background and the projects you owned
• Your reasons for moving into caregiving or health technology
• How your experience lines up with the stack and responsibilities the role lists

How to prepare

• Write a short walk-through of your last two projects, naming the stack (TypeScript, Python, AWS where true), your decisions and the outcome.
• Write an honest answer to why caregiving technology and why Abby Care, using one concrete detail about the platform, not a general mission statement.
• Prepare questions about how engineers work with the clinical, compliance and operations teams day to day.

Coding Challenge

The second stage is reported as a coding challenge that shows TypeScript proficiency. The reported coding questions include a live TypeScript exercise on data manipulation and API integration, a route-optimization algorithm for caregivers, SQL tuning, a rate-limiting middleware and debugging async Python. Reports do not say which of them appear in this round, so practise all of them. Aim for typed, validated code that handles malformed input, and say your complexity and edge cases out loud as you write.

What to demonstrate

• Working TypeScript written under test conditions
• Transforming data and calling APIs correctly, including failures
• Handling edge cases and explaining complexity as you code

How to prepare

• Write a TypeScript function that validates unknown JSON questionnaire payloads, groups responses by patient with a Map, and returns a typed success-or-error result.
• Implement a token-bucket rate limiter as TypeScript middleware that returns 429 with a retry hint, then explain how the counters would move to a shared store across instances.
• Practise asyncio in Python: a gather-based fetch with a concurrency limit, error handling and timeouts, and find a missing await by reading the code.
• Run a practice query through EXPLAIN, find a join that multiplies rows, and fix it with pre-aggregation and an index.

System Design Interview

The third stage is a system design interview. The reported design questions cover a notification service for urgent schedule updates, secure storage for patient records, scaling AWS infrastructure for a tenfold surge in active care sessions, monolith versus microservices for an early-stage healthcare platform, and real-time chat between caregivers and clinical support. Domain-modeling questions (questionnaires, patient location, vital-signs ingestion, offline entry) are also reported. Candidates report that the design questions are framed around healthcare standards, privacy and data integrity.

What to demonstrate

• Scoping an ambiguous healthcare problem and stating assumptions
• Choosing data stores and modeling relationships such as patient, caregiver, questionnaire and care setting
• Scalability, reliability and failure handling, with encryption, access control and audit logging included

How to prepare

• Sketch a data model for questionnaire assignments and a patient's location history, covering versioned templates, due dates, status and who changed what.
• Design the notification service end to end: queue, retries, deduplication, channel fallback, and how an urgent update differs from a routine one.
• Write a security checklist you apply to every design: encryption at rest and in transit, role-based access, audit logs, retention and deletion.
• Prepare a clear position on monolith versus microservices for a small team, including when you would split a service out.

Behavioral Assessment

Candidates report the final stage as a Behavioral Assessment with a product manager. The reported prompts cover balancing speed with regulatory compliance, working with clinical or operations teams, prioritizing a backlog under urgent requests, resolving a technical disagreement, and why you want to build caregiving technology here. Reports also list three values to be ready for: Families First, Urgency with Precision and Relentlessly Resourceful.

What to demonstrate

• Communication with non-engineering partners such as product, clinical and compliance colleagues
• How you prioritize and decide when requests compete
• How you handle disagreement and ownership from concept to launch

How to prepare

• Write one story per reported prompt in situation, decision, result form, written in first person singular.
• Explain one technical constraint to a non-engineer in plain language and have them repeat it back.
• Map each of the three reported values to a story where you made a trade-off, not just a slogan.

Where candidates lose points

Presenting a data model or ingestion endpoint with no word on how sensitive data is protected

Draw access control, encryption in transit and at rest, and an audit log into the first diagram of the patient-records, vital-signs and care-log questions. Say who can read each table and how a read is recorded.

Answering offline-first entry or device ingestion without a retry and conflict story

Give each client write a client-generated id, queue operations locally, make the server endpoint idempotent on that id, and state the merge rule when two edits collide. Say what the caregiver sees while a write is pending.

Writing TypeScript that trusts its input, using any or assuming payload fields exist

Accept unknown, narrow it with a validator, and return a typed success-or-error result. Test an empty list, a missing field, a duplicate id and an API timeout before the code is called done.

Starting to build before clarifying an open prompt such as modeling where a patient is in the home or hospital

Ask what states exist, who updates them, whether history matters and what the questionnaire list depends on. Write down your assumptions, then model. Then show how the model handles a patient who moves between settings.

Behavioral answers that say 'we' throughout, with no decision, or a mission speech that fits any health company

Name your decision, the counterpart (product, clinical, compliance or operations) and the result for every story. For the 'why Abby Care' answer, tie one platform fact and one reported technical problem to your own background.

Questions & practice

This category evaluates your fluency in core programming languages, specifically TypeScript and Python, and yo

Medium · Coding & Algorithms

This category evaluates your fluency in core programming languages, specifically TypeScript and Python, and your ability to write clean, maintainable code under test conditions.

Approach

1. Write typed code: in TypeScript use strict mode, model inputs with interfaces or discriminated unions and prefer unknown plus narrowing over any; in Python use type hints and dataclasses.
2. Know the standard tools without looking them up: Map, Set, reduce and comparator sorts in TypeScript (the default sort compares strings, so [10, 9, 1].sort() returns [1, 10, 9]); Counter, defaultdict, heapq, bisect and itertools in Python.
3. Async basics in both: Promise.all rejects on the first failure while Promise.allSettled waits for every result; asyncio.gather has return_exceptions; never block the event loop with synchronous I/O.
4. Keep functions small with early returns for bad input, then trace two cases aloud, including empty input and duplicates, before saying you are done.
5. State the complexity of what you wrote and know the traps, such as Array.shift being O(n) in general, so a queue should use an index pointer or a deque.

Follow-up

• How would you test this function?
• How would you change it to process a stream instead of an array?
• Where do TypeScript types fail to protect you at runtime, and what do you add?

Write an algorithm to optimize caregiver scheduling routes across a metropolitan area.

Medium · Coding & Algorithms

Approach

1. Name the problem: assigning and ordering visits across caregivers with time windows is a vehicle routing problem with time windows, and one caregiver's ordering alone is a travelling salesman problem; both are NP-hard, so propose a heuristic.
2. Clarify inputs: visits (location, time window, duration, required skills), caregivers (start point, shift hours), and a travel-time matrix from a routing service, since straight-line distance is poor in a city.
3. Baseline: nearest-neighbour construction, building each caregiver's route from their start point by repeatedly appending the nearest visit still reachable inside its window. With a precomputed travel-time matrix that is O(n²) in total over n visits. Cheapest insertion, which places each visit at the feasible position that adds the least travel time, is a stronger alternative baseline at higher cost.
4. Improve with local search: 2-opt (reverse a segment when it shortens the route and stays feasible), plus relocate and swap moves between caregivers, stopping when nothing improves or a compute budget runs out. For a handful of stops, Held-Karp dynamic programming is exact in O(n²·2ⁿ).
5. Raise the practical constraints: caregiver continuity for a patient, skill matching, overtime limits, and that a solver library such as OR-Tools is a sensible production choice.

Follow-up

• A visit cancels mid-morning: do you re-solve from scratch or repair the existing routes?
• No caregiver can reach a visit inside its window: what does the algorithm return?
• How would you judge whether the heuristic's routes are good enough?

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

1. Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
2. Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
3. State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
4. Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

• The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
• Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Optimize a slow-running SQL query that generates patient health outcome summaries.

Medium · Coding & Algorithms

Approach

1. Read the plan first with EXPLAIN (ANALYZE, BUFFERS) in Postgres or the engine's equivalent: look for sequential scans on large tables, row estimates far from actuals, nested loops over many rows and sorts spilling to disk.
2. Index for the filter and join, for example (patient_id, recorded_at), and keep predicates sargable: recorded_at >= '2026-01-01' AND recorded_at < '2026-02-01' can use the index, a function wrapped around recorded_at cannot. Select only the columns needed.
3. Check for row multiplication: joining patients to visits and to measurements before aggregating inflates sums and counts, so pre-aggregate each child table in a subquery or CTE and join the results. Rewrite correlated subqueries as a join plus GROUP BY.
4. If the summary is read often and the data changes slowly, precompute it in a materialized view or an incrementally maintained summary table, trading freshness for speed.
5. Refresh statistics with ANALYZE and compare plans and timings before and after on production-sized data.

Follow-up

• The index exists but the planner ignores it: what could explain that?
• How would you keep a materialized summary fresh without blocking readers?
• How does the query change if results must be limited to patients the requesting user may see?

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?

This category tests your ability to translate complex real-world healthcare workflows into robust data models

Medium · Technical & Domain Modeling

This category tests your ability to translate complex real-world healthcare workflows into robust data models and functional software architectures.

Approach

1. Start from the nouns and their lifecycle: list the entities (patient, caregiver, care setting, questionnaire, visit) and the events that change them, then decide which become tables and which become state transitions.
2. Model state as an explicit enum plus an append-only history table (status, changed_at, changed_by), so the current state is derivable and the audit trail comes with it.
3. Separate definitions from instances: a versioned template table and an instance table that pins the version it was created from, so editing a template never rewrites past records.
4. Push invariants into the database where you can: foreign keys and check constraints, plus a partial unique index for rules such as one open instance per patient per template (CREATE UNIQUE INDEX ... ON instance (patient_id, template_id) WHERE status = 'open'), because a plain UNIQUE constraint cannot express that rule. Validate the rest at the API boundary.
5. Name the queries the model must answer quickly and index for them, for example (patient_id, status) for everything outstanding on one patient.

Follow-up

• How would the model change if a patient has two caregivers with different permissions?
• How would you add a new state to a live table without downtime?

Configure a list of questionnaires to complete for a patient. Model where the patient is in the home or hospit

Medium · Technical & Domain Modeling

Configure a list of questionnaires to complete for a patient. Model where the patient is in the home or hospital.

Approach

1. Clarify first: who assigns questionnaires, whether the set depends on the care setting, how often each recurs (daily, per visit, once per stay) and who completes it (family caregiver or clinician).
2. Entities: Patient; CareEpisode with a setting enum (home, hospital) and start/end timestamps, so location is a time-ranged history instead of one mutable column; versioned QuestionnaireTemplate; AssignmentRule (template, setting, cadence); QuestionnaireInstance (patient, template version, due_at, status, completed_by).
3. Build the to-do list by joining the open episode (end_at IS NULL) to the rules for its setting. On admission or discharge, close the old episode, cancel or keep pending instances per rule and create new ones in a single transaction.
4. Edge cases: a transfer mid-questionnaire, overlapping episodes (block with an exclusion constraint or an application check), due dates in the patient's timezone, and a template required in both settings not being duplicated (unique on patient, template, due period).
5. In TypeScript, represent location as a discriminated union ({kind: 'home', ...} | {kind: 'hospital', facilityId, admittedAt}) so the compiler forces every branch to be handled.

Follow-up

• How would clinical staff add a new questionnaire without a code deploy?
• What happens to a half-completed questionnaire when the patient is admitted to hospital?
• How would you list every overdue questionnaire across all patients efficiently?

Design an API endpoint to ingest real-time vital signs from in-home monitoring devices.

Medium · Technical & Domain Modeling

Approach

1. Contract: POST /v1/devices/{deviceId}/readings taking a batch, each reading with a device-generated id, type (heart rate, SpO2, blood pressure), value, unit and measured_at in UTC. Authenticate each device with its own credential (mTLS or a signed token), never a shared key.
2. Idempotency: a unique key on (device_id, reading_id) so a retry after a timeout inserts nothing twice; return 202 with per-item results so a partly invalid batch is not all-or-nothing.
3. Validate at the edge (schema, units, physiologically plausible ranges, timestamps not far in the future), then hand accepted readings to a queue or stream so a slow database never backs up devices.
4. Keep storage and alerting apart: raw readings go to a time-partitioned store keyed by patient and time; a separate consumer evaluates thresholds and triggers notifications, so changing an alert rule never touches ingestion.
5. Order by measured_at, not received_at, and store both, because devices that were offline deliver late and out of order. Encrypt in transit and log who reads the data.

Follow-up

• A device was offline for a day and uploads 10,000 readings at once: what changes?
• How would you version the payload when a new device type adds fields?
• How do you make sure one abnormal reading raises exactly one alert?

How would you handle state management for offline-first data entry by family caregivers in low-connectivity ar

Medium · Technical & Domain Modeling

How would you handle state management for offline-first data entry by family caregivers in low-connectivity areas?

Approach

1. Make the on-device store the source of truth (IndexedDB in a web app, SQLite on mobile): the UI reads and writes locally and never waits on the network.
2. Record every change in an outbox as an operation with a client-generated UUID, entity id and base version; a sync worker sends them in order when connectivity returns, with exponential backoff and jitter, and the server applies each operation idempotently by its id.
3. Choose conflict handling per data type: append-only care log entries cannot conflict; for editable records, detect conflicts with a version number and surface them for review instead of silently overwriting. Last-writer-wins is simple but loses edits; CRDTs avoid that at a real complexity cost.
4. Do not order events by device clocks, which drift: keep device time for display and use server-assigned versions for conflict decisions.
5. Encrypt the local store, cache only the patients this caregiver serves, decide what happens to unsynced changes on logout or session expiry, and show sync status in the UI.

Follow-up

• Two caregivers edit the same visit note offline: what does each see after sync?
• How do you migrate the local schema when an app update ships while changes are still unsynced?
• The server rejects a queued change as invalid after the caregiver has gone home: what happens next?

Implement a data validation pipeline for compliance reporting in healthcare.

Medium · Technical & Domain Modeling

Approach

1. Clarify the report: which fields and sources feed it, its deadline, and what an invalid record does (blocks the report, is quarantined, or is included with a flag).
2. Stages: land raw records unchanged for audit, then schema validation (pydantic in Python or zod in TypeScript), then business rules (visit end after start, caregiver credential valid on the visit date, hours within limits), then cross-record checks (duplicates, overlapping visits for one caregiver), then output.
3. Treat rules as data: each has an id, a severity (error or warning) and a message; collect every failure per record instead of stopping at the first, so one run reports all problems.
4. Quarantine failures with reason codes in their own table, make reruns idempotent (keyed on source record id plus rule version), and keep lineage of which rule version passed which record.
5. Test each rule with fixtures at its boundaries (dates, timezones, nulls) and track rejection counts per rule, so an upstream format change shows up as a spike.

Follow-up

• A rule changes mid-quarter: how do you revalidate records that already passed?
• How would this run on millions of records a day?
• Who fixes quarantined records, and how does the correction flow back into the report?

Write a function to parse and aggregate patient care logs securely.

Medium · Technical & Domain Modeling

Approach

1. Clarify the format (JSON lines, CSV), the size (stream or load) and the aggregation keys, such as per patient per day or hours per caregiver.
2. Stream the input line by line (a Python generator over the file, or Node's readline) instead of loading it whole; parse each line inside try/except and record malformed lines by line number instead of aborting.
3. Aggregate in a dict keyed by (patient_id, local_date) with defaultdict and split entries that cross midnight; one pass is O(n) time. Dedup on log id needs care: a global seen-id set grows with the number of distinct entries, so memory then scales with the file, not the number of groups. Bound it with per-group id sets flushed when a day closes (date-ordered input), adjacent dedup on input sorted by id, or a bounded window if duplicates arrive close together; at 50 GB, use an external sort or a table with a UNIQUE constraint on log id.
4. Security: validate types and ranges and never eval input; keep health data out of error logs (log line number and field name only); return only patients the caller is authorised for; guard against path traversal on filenames and formula injection if output reaches a spreadsheet (cells starting with =, +, - or @).
5. Unit tests: empty file, malformed line, duplicate entries, an entry spanning midnight, and a patient the caller may not see.

Follow-up

• The file is 50 GB: what changes?
• Logs are corrected after the fact: how do you keep the aggregates reproducible?
• How would you show that no health data ended up in application logs?

Complete a live TypeScript coding exercise focusing on data manipulation and API integration.

Medium · Coding & Algorithms

Approach

1. Expect a shape like: fetch JSON from one or more endpoints, transform it (filter, group, join, sort) and return a result. Define types for the response and validate at runtime (zod or a type guard), because an as-cast checks nothing.
2. Use async/await with fetch and check response.ok: fetch rejects only on network failure, not on 4xx or 5xx. Loop on the cursor or page number until the API reports no more pages.
3. Run independent requests concurrently with Promise.all, cap concurrency when there are many calls, and set timeouts with AbortController.
4. Group and join with a Map in O(n + m) instead of nested find calls in O(n·m); Array.prototype.sort is stable since ES2019; parse ISO dates explicitly and work in UTC.
5. Say the edge cases as you code: empty pages, missing optional fields, duplicate ids, and retry with backoff on 429 or 5xx only.

Follow-up

• The API returns 429 halfway through pagination: what does your code do?
• How would you unit test this without calling the network?
• Where would you add caching, and how would you invalidate it?

Implement a rate-limiting middleware for backend API protection.

Medium · Coding & Algorithms

Approach

1. Clarify the key (API key, user id or IP), limits per route, whether there is one instance or many, and the response: 429 with a Retry-After header.
2. Compare algorithms: a fixed-window counter is simple but allows up to twice the limit across a window boundary; a sliding-window log is exact but stores every request; a sliding-window counter approximates it cheaply; a token bucket allows controlled bursts with O(1) state per key. Choose and justify one.
3. Token bucket as middleware (req, res, next): store tokens and last-refill time per key; on each request set tokens = min(capacity, tokens + elapsed × rate); if tokens ≥ 1, decrement and call next(), otherwise return 429.
4. Across several instances, in-memory counters diverge, so keep state in Redis and make check-and-decrement atomic with a Lua script (or INCR plus EXPIRE for a fixed window), with TTLs so idle keys expire.
5. Decide fail-open or fail-closed per route when the store is unreachable, send remaining-quota headers, and test with an injected clock.

Follow-up

• How do you stop one client's burst from starving everyone else?
• Two requests for the same key reach two instances at the same moment: what happens?
• Should the limiter fail open or closed when Redis is down, and why?

This category assesses your capability to design scalable, secure, and resilient distributed systems that adhe

Medium · System Design & Architecture

This category assesses your capability to design scalable, secure, and resilient distributed systems that adhere to healthcare industry standards.

Approach

1. Use a fixed order: requirements and scale estimates said aloud, API, data model, components, then deep dives on the bottleneck, failure modes and security.
2. Treat security as components, not a closing remark: TLS in transit, KMS-managed encryption at rest, least-privilege IAM, access checks tied to the patient-caregiver relationship, an audit log of every read and write of health data, and health data kept out of logs and analytics.
3. Resilience: stateless services behind a load balancer across availability zones, a managed database with replicas and automated backups (state your RPO and RTO), and queues with retries, idempotency keys and dead-letter queues between services.
4. Scale: separate read and write paths, cache read-heavy non-sensitive data, partition by patient or organisation, and move anything not user-facing to asynchronous workers.
5. Close with the trade-offs you accepted and the signals you would monitor: latency percentiles, error rate and queue depth.

Follow-up

• Where does the audit log live so no one can alter it?
• How would you recover if the primary database's region became unavailable?
• Which component do you scale first under load, and how do you know?

Design a scalable notification service to alert family caregivers of urgent medical schedule updates.

Medium · System Design & Architecture

Approach

1. Requirements: urgent schedule changes must arrive quickly and reliably across push, SMS and email, honour per-user preferences, and be delivered at least once without visible duplicates.
2. Flow: the scheduling service publishes a schedule-changed event to a topic; the notification service resolves recipients and preferences and enqueues per-channel jobs; channel workers call push (APNs/FCM), SMS and email providers and record delivery status.
3. Give urgent alerts their own queue so a bulk send cannot delay them, and escalate when a push is not acknowledged in time: push, then SMS, then a person on the operations side.
4. Reliability: an idempotency key per (event, recipient, channel), exponential backoff, dead-letter queues, a fallback provider, and coalescing of rapid repeated edits into one message.
5. Privacy: keep health details out of push and SMS bodies, which show on lock screens; send a generic message with a deep link into the authenticated app, and keep a delivery log for audit.

Follow-up

• How do you know a caregiver actually saw an urgent alert?
• The SMS provider is down for an hour: what happens to queued alerts?
• A schedule is edited five times in a minute: how many notifications go out?

Architect a secure document storage system for sensitive patient health records compliant with privacy regulat

Medium · System Design & Architecture

Architect a secure document storage system for sensitive patient health records compliant with privacy regulations.

Approach

1. Requirements: upload, view and share records (care plans, scans, forms); define who may read them (the patient, assigned caregivers, clinical staff), retention periods and audit needs.
2. Store blobs in a private object store such as S3 with public access blocked and KMS server-side encryption (envelope encryption, optionally per-tenant keys); keep metadata (patient, type, uploader, version, checksum) in a relational database.
3. Every access goes through an API that checks authorisation and then issues a short-lived pre-signed URL; the bucket is never exposed directly, and services get least-privilege roles.
4. Log every access (who, what, when) to append-only storage; enable versioning and object lock against deletion; scan uploads for malware and verify content type and checksum.
5. State the compliance controls: encryption in transit and at rest, access control, audit logs, backups, retention and deletion rules, and, under HIPAA in the United States, business associate agreements with vendors that handle health data.

Follow-up

• A caregiver leaves: how do you revoke access, including links already issued?
• How would you support search over document contents without weakening security?
• A patient requests deletion but retention rules require keeping the record: how does the design handle both?

How would you scale an AWS-based infrastructure to support a 10x surge in active family care sessions?

Medium · System Design & Architecture

Approach

1. Measure before scaling: current peak load, which resource saturates first (database CPU or connections, app CPU, third-party limits), and what 10x means here (concurrent sessions, writes per second).
2. Stateless app tier: ECS services and EC2 Auto Scaling groups scale on CPU or ALB request count per target across availability zones. Lambda scales differently, per request up to its concurrency limits, so check the account concurrency quota and set reserved or provisioned concurrency before a tenfold surge. Move session state into Redis, DynamoDB or tokens.
3. The database usually breaks first: add read replicas for read-heavy queries, pool connections (RDS Proxy or PgBouncer, since many containers or Lambdas exhaust connections), scale vertically, cache with ElastiCache, and partition only if still needed.
4. Move non-interactive work to SQS consumers, serve static assets from CloudFront, and check AWS service quotas and third-party rate limits well ahead of the surge.
5. Load-test at 10x in staging, use scheduled scaling for predictable peaks, set alarms on saturation signals, and estimate the cost of the new footprint.

Follow-up

• Autoscaling reacts too slowly to a sudden spike: what do you change?
• What fails first in the database at 10x, and which metric tells you?
• How do you keep cost from growing tenfold along with traffic?

Discuss the trade-offs between monolithic and microservices architectures for an early-stage healthcare platfo

Medium · System Design & Architecture

Discuss the trade-offs between monolithic and microservices architectures for an early-stage healthcare platform.

Approach

1. Monolith: one deployable and one database, local transactions, simpler debugging, deployment and onboarding; the fastest path for a small team whose product is still changing shape.
2. Microservices: independent deploys, scaling and fault isolation, at the cost of network calls, distributed transactions (sagas), eventual consistency, heavier observability and infrastructure, and more surfaces to secure and audit.
3. Healthcare angle: fewer places holding health data makes access control and audit simpler, while isolating the most sensitive records in one service and store can shrink the compliance boundary.
4. Recommend a modular monolith with enforced module boundaries (no reaching into another module's tables) and extract a service only for a concrete reason: a different scaling profile such as device ingestion, a separate team, or a separate compliance boundary.
5. Explain extraction: the strangler pattern, events between modules, and contract tests at the new interface.

Follow-up

• Which part would you extract first, and why?
• How do you keep module boundaries from eroding inside a monolith?
• How do you handle a transaction that spans two services after the split?

Design a real-time chat feature connecting family caregivers with clinical support staff.

Medium · System Design & Architecture

Approach

1. Requirements: conversations between caregivers and support staff, delivery and read receipts, history, attachments, presence, and routing of new conversations to available staff.
2. Transport: WebSockets through a connection gateway; the server persists a message before fanning it out, and falls back to a push notification when the recipient is offline.
3. Data: messages keyed by (conversation_id, sequence) with a per-conversation increasing sequence for ordering and a client-generated message id for idempotent retries; on reconnect a client fetches everything after its last sequence.
4. Scale: gateways hold connections, so route messages through pub/sub (Redis or a managed broker) to whichever gateway holds the recipient, and partition storage by conversation.
5. Healthcare concerns: encryption in transit and at rest, access tied to the care relationship, retention and audit, no health details in push previews, and an escalation path when no staff member picks up an urgent message.

Follow-up

• How do you keep messages in order when a sender's retry arrives late?
• A support agent disconnects mid-conversation: how is the chat reassigned?
• How do read receipts stay consistent across a user's devices?

Debug an asynchronous data-fetching routine in Python.

Medium · Coding & Algorithms

Approach

1. Reproduce and classify the symptom: wrong data, a hang, a 'coroutine was never awaited' warning, a 'Task exception was never retrieved' message, or calls that are slower than expected.
2. Check the usual causes: a missing await (you get a coroutine object, not data); blocking calls such as requests or time.sleep inside async code, which stall the event loop (use an async client like httpx.AsyncClient or aiohttp, or asyncio.to_thread); awaiting inside a for loop so calls run one at a time (use asyncio.gather).
3. Check errors and lifetimes: by default gather propagates the first exception while the other awaitables keep running; a create_task result with no saved reference can be garbage collected; add timeouts with asyncio.wait_for or asyncio.timeout (3.11+).
4. Check shared state and limits: a dict or list mutated across await points can interleave unexpectedly; cap concurrency with asyncio.Semaphore and reuse one client session instead of opening one per request.
5. Use asyncio debug mode (asyncio.run(debug=True) or PYTHONASYNCIODEBUG=1) to log slow callbacks and unawaited coroutines, then lock the fix in with a pytest-asyncio test against mocked responses.

Follow-up

• How would you add retries with backoff without retrying non-idempotent calls?
• It works locally but hangs in production: what do you check?
• How do you cap the routine at ten concurrent requests?

Suggested practice checklist

The plan starts with the four reported rounds, spends two days on TypeScript, Python and SQL coding, a day each on data modeling, algorithms and system design, and ends with behavioral rehearsal and a mixed mock.

Day 1 — Map the loop and write your opening story

• List the four reported rounds and, beside each, the reported question types you would practise for it. Mark your weakest.
• Write a walk-through of your two most relevant projects, with the stack, your decisions and the result.
• Write your answer to why you want to build caregiving technology at Abby Care, with one concrete platform detail.

Deliverable: A one-page round map with the weakest round marked, a project walk-through, and a written 'why Abby Care' answer.

Day 2 — TypeScript coding: data, APIs and rate limiting

• Write a TypeScript function that validates unknown questionnaire JSON, groups responses by patient using a Map, and posts them with fetch, returning a typed success-or-error union.
• Implement a token-bucket rate-limiting middleware in TypeScript that returns 429 with a retry hint, and write tests for burst, refill and two API keys.

Deliverable: A tested validation-and-post function, a tested rate limiter, and notes on the sliding-window practice problem.

Day 3 — Python async, SQL tuning and secure log parsing

• Write a Python asyncio fetch routine with a semaphore, timeouts and gather, then plant three bugs (missing await, swallowed exception, unbounded concurrency) and find them again from the code and logs.
• Take a query that joins patients, visits and questionnaire responses to summarize outcomes. Read its EXPLAIN output, find any join that multiplies rows, then fix it with pre-aggregation and an index.

Deliverable: A debugged asyncio routine with a bug list, a before-and-after query plan, and a log parser with tests that confirm nothing sensitive is printed.

Day 4 — Domain modeling, API contracts and UI state

• Sketch tables for questionnaire templates (versioned), assignments with due date and status, responses, and a patient location history that distinguishes home from hospital with start and end times.
• Write the contract for an endpoint that accepts vital-signs readings from home devices: batch payload, device identity, idempotency key, 202 versus 200, and an error list callers can act on.
• Design offline-first data entry for caregivers: local store, operation queue, client-generated ids, sync order and a conflict rule. State what the caregiver sees while a write is pending.
• Build a small typed TypeScript questionnaire form component whose state models each answer as saved, pending sync or failed, and show that status to the caregiver.

Deliverable: A schema diagram, an endpoint contract with error cases, a one-page offline sync design with its conflict rule, and a typed form component with pending-sync status.

Day 5 — Route optimization and the validation pipeline

• Write a caregiver route-scheduling algorithm: state the assumptions (visit windows, travel times, one caregiver or several), give a greedy nearest-neighbor baseline, and explain how you would improve it and why the exact version is expensive.
• Write a data validation pipeline for compliance reports: schema checks, business-rule checks, a quarantine for rejected rows and a summary of failures. Cover reruns without duplicates.
• Run both aloud, covering complexity and edge cases, and note every place you hesitated.

Deliverable: A route algorithm with a stated baseline and complexity, a validation pipeline with rejection handling, and a list of hesitations to fix.

Day 6 — System design on AWS with security built in

• Design the urgent-update notification service and the caregiver-to-clinical chat: queues, retries, deduplication, delivery state, channel fallback and a path for urgent messages.
• Design secure patient-records storage: encryption at rest and in transit, role-based access, audit trail, retention and deletion.
• Answer the tenfold-surge and monolith-versus-microservices questions: find the first bottleneck on each path, then give your choice for a small early-stage team.
• Outline a CI/CD pipeline on AWS: build and test stages, secrets held in a secrets manager rather than in the repository, and deploy gates such as required tests, an approval step for production and automatic rollback.

Deliverable: Three annotated design sketches (messaging, records storage, scaling), a one-paragraph position on monolith versus microservices, and a one-page CI/CD outline.

Day 7 — Behavioral rehearsal and mixed mock

• Write one story for each of the five reported behavioral prompts, with your decision and a measured result. Map the three reported values to those stories.
• Tell two stories to a non-engineer and ask them to restate the decision and trade-off. Rewrite what they could not repeat.
• Run a mixed mock: one coding problem, one design question and one behavioral story, back to back.

Deliverable: Five written stories, a list of rewrites from the non-engineer listener, and notes from the mixed mock.

Behavioral preparation for the product-manager round

Candidates describe the last stage as a Behavioral Assessment with a product manager that covers interpersonal skills. The reported prompts focus on work with clinical, operations and compliance partners, trade-offs between speed and regulation, and prioritization under competing requests. Reports also list the values Families First, Urgency with Precision and Relentlessly Resourceful to be ready for. Prepare each story with your own decision and a measurable result.

1. Tell me about a time you had to ship a feature rapidly while maintaining high standards for code quality and reliability.

This category examines how you communicate, align with cross-functional partners, and embody company values wh

Medium · Behavioral & Product Collaboration

This category examines how you communicate, align with cross-functional partners, and embody company values when building products.

Approach

1. This prompt names a category of behavioral questions rather than one question, so prepare one anchor story that covers communication, cross-functional alignment and values. Choose a product you built with a non-engineering partner (product, clinical, compliance or operations) and tell it as goal, constraint, decision, result.
2. Show translation: name one technical constraint and the plain-language trade-off you gave the partner, for example that offline entry means a visit note can reach the clinician later than they expect, and what option they chose.
3. Name the written artifact that kept everyone aligned, such as a one-page decision record, acceptance criteria or a status definition that both teams used.
4. Tie any value to an action, not a phrase. Reports list Families First, Urgency with Precision and Relentlessly Resourceful, so keep one example of a decision where the end user's need changed what you built.
5. Avoid a story where 'we aligned' hides who disagreed and who decided. Say what you did in first person singular.

Follow-up

• What did the other team want that you could not give them, and how did you tell them?
• How did you find out your explanation had not landed?
• What would you change about how you communicated on that project?

Tell me about a time you had to balance engineering velocity with strict regulatory compliance.

Medium · Behavioral & Product Collaboration

Approach

1. Pick a story with a real, nameable constraint: access control on sensitive data, audit logging, retention, consent or a security review. If you lack healthcare experience, payments, SOC 2 or privacy-deletion work shows the same tension. Name the requirement precisely.
2. State the pressure (the launch date and who wanted it), who owned the requirement, and the options you laid out: ship in full, ship a narrower slice, or move the date. Say which you chose and why.
3. Show the technique that preserved speed: shipping the part that touched no sensitive data first, putting the rest behind a feature flag, automating checks in CI (for example blocking sensitive fields from log output), and bringing the compliance owner in with a written spec early.
4. Close with the result and the honest cost: what shipped and when, what you deferred, and how the deferral was tracked.
5. The mistake that sinks it is describing compliance as an obstacle you worked around, or a story with no trade-off.

Follow-up

• Which requirement could you never trade away, and which one was negotiable?
• How did you get sign-off from compliance without repeated rounds of review?
• What did you defer, and how did you make sure it was finished?

Describe a situation where you collaborated with clinical or operations teams to ship a critical feature.

Medium · Behavioral & Product Collaboration

Approach

1. Choose a feature where a clinical, operations, support or other domain team's workflow shaped the design. If you have no clinical partner, an operations or support team works as long as the feature changed because of them.
2. Show how you learned their workflow: watching them use the existing tool, reading their runbooks or sitting in on their calls. Give one thing that surprised you.
3. Describe one design decision that changed because of it, such as a field order, a manual override, a status they needed or an alert rule.
4. Cover launch: a pilot group, a feature flag, a feedback channel, and the metric you watched (error rate, tickets, time per task). State what they told you afterwards.
5. Avoid treating them as stakeholders who handed you requirements. The story needs a moment where you changed your mind.

Follow-up

• What did they ask for that you pushed back on, and how was it settled?
• How did you handle problems they reported after launch?
• How did you know the feature actually helped them?

How do you prioritize your engineering backlog when multiple stakeholders request urgent changes?

Medium · Behavioral & Product Collaboration

Approach

1. State your method first. For each request, write who is affected, what happens if it waits a week, the effort and whether it can be undone. Rank by cost of delay against effort, with a fixed override for safety, data-integrity and regulatory-deadline items.
2. Make the trade-off visible: keep one shared list, say what slips and by how much, and when two requesters' claims truly conflict, take it to the product owner to decide rather than choosing silently.
3. Define 'urgent' in advance (production-impacting, user-facing, with a hard external date), reserve some capacity for it and keep interrupts out of the rest of the sprint.
4. Give one real instance: the two asks, what you chose, what you told the person you declined, and what happened.
5. The mistake that sinks it is saying yes to everyone or following whoever is loudest.

Follow-up

• Two senior stakeholders each insist theirs comes first. What do you do?
• An urgent request arrives mid-sprint. What gets dropped, and who is told?
• How do you stop technical debt from always losing to feature requests?

Share an example of a technical disagreement with a teammate and how you resolved it constructively.

Medium · Behavioral & Product Collaboration

Approach

1. Pick a disagreement with technical substance, such as a queue versus a direct call, a schema shape or a service split, not a clash of personalities.
2. State each position fairly in a sentence, then the criteria you agreed to decide on: latency, operational cost, delivery date, reversibility.
3. Resolve it with evidence: a spike, prototype, benchmark or load test, or a reversible choice with a checkpoint. Say who made the final call and how it was recorded.
4. Report the outcome honestly, including if the other view turned out to be right, and how you worked together afterwards.
5. Avoid a story in which you were right and the teammate was stubborn, or one with no resolution.

Follow-up

• What would you have done if the data had come out against you?
• If you had been overruled, how would you have committed to the decision?
• What did you take from your teammate's position?

Why do you want to build caregiving technology at Abby Care specifically?

Medium · Behavioral & Product Collaboration

Approach

1. Build the answer from facts you can state: the platform trains and employs family caregivers so they are paid for home care, and engineers work with clinical, compliance and operations colleagues. React to one or two of these in your own words.
2. Link it to your own history only if true: personal experience with caregiving, health technology, or regulated and sensitive data systems you have built.
3. Connect to the work. Pick a technical problem from the reported questions, such as offline data entry for caregivers with weak connectivity or urgent schedule notifications, and say why your experience fits it.
4. Keep values to one sentence with an example. A mission speech that would fit any health startup is the mistake to avoid.
5. Close with what you want to learn in the first months, which shows you thought about the job rather than the headline.

Follow-up

• What worries you about working in healthcare, and how would you handle it?
• Which part of your last role best prepares you for this platform?
• What would you want to understand first about how caregivers use the product?