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How would you instrument an application to calculate Customer Lifetime Value?
Tests whether you tie revenue and cost events to persistent identity and model cohort retention. Outline: track purchases, CAC, and churn with persistent IDs; project value via retention curves.
Design a system to handle subscription renewals
States include active, past_due, and cancelled; transitions are payment success, dunning exhaustion, and grace expiry; use idempotent webhooks.
How do you ensure consistent A/B test variants across sessions and devices?
Tests stable identity and delayed attribution. Fix: one stable user ID for SDK bucketing, persisted across devices via login or first-party cookies, attached to every conversion event. Never use per-device randomization or third-party cookies.
Model a 3-month 20% promo discount and apply it at billing
This tests separating coupon rules from per-user redemptions. Good answers use a coupons table for the 20%/3-month rule, a redemptions table for usage, and apply the discount to the first three invoices. A red flag is hard-coding the discount on the user row.
What schema changes are needed to add a Pro subscription tier?
This tests normalization of billing data versus hardcoding tiers. Add a plans table with integer cents pricing, link subscriptions via plan_id, and leave users untouched. Red flag: adding a tier string column to users or storing prices in subscriptions.
Design a real-time personalized notification trigger system
Stream events to a delayed queue, expose a rule UI to non-technical users, and deliver idempotently.
Compare fan-out-on-write vs fan-out-on-read for an activity feed
Tests whether you tie feed architecture to read/write ratios and follower distribution. Strong answers contrast push O(1) reads with celebrity storms against pull O(1) writes with read amplification, then propose a hybrid threshold.
Outline architecture for a weekly email digest of unread notifications
This tests batch processing and scheduled delivery at scale. Pre-aggregate unread counts, shard digest jobs across a distributed scheduler, and cache unsubscribes for fast filtering. Never scan the notifications table at send time for millions of users.
Design an A/B test for a Buy Now button
This tests experiment plumbing: deterministic bucketing, sticky storage, and logging. A strong answer covers user-ID hashing, cookie persistence, and impression-plus-conversion events.
Describe the data model and backend logic for a daily login bonus.
This tests streak state machines and calendar edge cases. A strong answer stores last_login_utc and streak_count, uses UTC day buckets, resolves timezones per user tz, and needs no leap-year logic.
Describe the end-to-end data flow for tracking a 'Share' button click
Payload carries event type, user ID, timestamp, device, content; client batches with retry; backend validates and lands in a partitioned store.
Long-term onboarding holdback: technical and data integrity challenges
This tests the engineering cost of year-long holdbacks in growth. A strong answer covers feature-flag entropy, pipeline drift, survivorship bias, and counterfactual validity. Red flag: treating the holdback as static config that never rots.
Design a role-based personalized onboarding system
Tests separation of content and logic for scalable personalization. Strong answer: CMS-backed rule engine, multi-channel delivery, event-driven triggers, and per-segment metrics. Red flag: hardcoding role-specific UI components in the client.
How would you design resumable multi-step onboarding state management?
This tests cross-device onboarding resume. A strong answer uses debounced server sync for cross-device resume with localStorage fallback, covers anonymous users, and handles conflicts. Red flag: pure client or server storage ignoring offline gaps or privacy.
How would you validate that early Project creation drives retention?
Tests causal rigor on behavioral predictors. Good answer: define D30 retention and the 24-hour treatment; pull timestamps and covariates; cohort-compare with propensity matching; show lift with confidence intervals and propose an A/B nudge.
How do you determine if a user is 'new' for a setup guide?
This tests whether you separate account age from user state for onboarding. Good answers compare created_at (brittle) with a persistent flag (idempotent) and consider milestones. A red flag is using a timestamp as a permanent new proxy without managing reruns.
How would you instrument a 4-step onboarding wizard?
Track Step Started and Step Completed with step_index and flow_variant; tie via distinct_id.
Design a programmatic SEO system for 1 million landing pages
Tests data infrastructure thinking, not content generation. Covers one-row-one-page schema, template rendering with edge caching, hierarchical routing, and crawl-budget controls via sitemaps. Red flag: AI bulk writing without structured data or caching.
Architect an A/B test for paid-ad signup flows
Tests pre-auth bucketing and funnel attribution. Hash a stable anonymous ID for fast assignment; stream events via Kafka into hourly aggregates; run t-tests on signup rates. Red flag: assigning after signup starts or DB lookups per assignment.
How would you implement a last-touch attribution model for user signups?
Tests your ability to translate marketing concepts into warehouse SQL. A strong answer covers UTM/pageview events, sessionized tables, and a windowed join for the last touch within 30 days of signup.