Company Product and Design Knowledge Questions

Demonstrate a well researched understanding of the company, its major products, target users, market position, and core business model, combined with familiarity with the company design philosophy and visible product design patterns. Prepare to speak about flagship products and features, typical user demographics and needs, the engineering or product challenges the company faces, and how those constraints shape product and design decisions. For design roles, be ready to articulate what you admire about the company design aesthetic, specific patterns or interactions you observe, accessibility and usability trade offs, and how your own design sensibilities or past work align with and could contribute to that aesthetic. For non design roles, emphasize product priorities, technical or operational challenges, and how your skills would help advance those products. Cite concrete examples such as a recent feature, a product workflow, a known engineering challenge, or public design documentation to show you have done focused research.

MediumTechnical

61 practiced

Create a JSON event schema example (two sample payloads) to instrument an onboarding funnel consisting of steps: 'signup', 'email_verified', and 'first_action'. Include fields for user identifiers (anonymous and known), device/platform, schema_version, experiment metadata, and an extensible properties object for future fields.

Sample Answer

Below is a concise, versioned JSON event schema for an onboarding funnel with two example payloads (one for signup, one for first_action). Fields included: event_type, schema_version, timestamp, user identifiers (anonymous_id, user_id), device/platform, experiment metadata, and an extensible properties object for future fields.

json

{
  "event_type": "signup",
  "schema_version": "onboard_v1",
  "timestamp": "2025-06-01T12:34:56Z",
  "anonymous_id": "anon_12345",
  "user_id": null,
  "device": {
    "platform": "web",
    "user_agent": "Mozilla/5.0 (...)",
    "client_version": "1.4.2"
  },
  "experiment": {
    "experiment_id": "exp_signup_cta",
    "variant": "B",
    "cohort": "test_group_2025_06"
  },
  "properties": {
    "referrer": "google.com",
    "utm": {
      "source": "google",
      "medium": "cpc",
      "campaign": "summer_promo"
    }
  }
}

json

{
  "event_type": "first_action",
  "schema_version": "onboard_v1",
  "timestamp": "2025-06-02T09:10:11Z",
  "anonymous_id": "anon_12345",
  "user_id": "user_98765",
  "device": {
    "platform": "ios",
    "os_version": "17.2",
    "app_version": "2.0.0"
  },
  "experiment": {
    "experiment_id": "exp_welcome_flow",
    "variant": "control",
    "exposure_time": "2025-06-01T12:35:10Z"
  },
  "properties": {
    "action_type": "create_project",
    "action_value": {"project_id": "proj_555"},
    "time_to_first_action_seconds": 123,
    "notes": null
  }
}

Key notes:- schema_version allows evolution; use a migration plan for changes.- anonymous_id persists before authentication; user_id populated after verify.- experiment object captures exposure metadata for attribution.- properties is a free-form map for extensibility; prefer nested sub-objects and consistent naming conventions to aid ETL.

HardSystem Design

54 practiced

The company's UI uses many micro-interactions (hover previews, tiny animations) that generate high event volume but low business signal. Propose a data architecture and processing strategy to compute reliable business metrics without incurring excessive storage/compute costs, while preserving the ability to investigate micro-interaction issues when needed.

Sample Answer

Requirements & constraints:- Accurate business metrics (e.g., DAU, conversion funnels) must not be biased by high-volume low-signal micro-interaction events.- Keep storage/compute costs low.- Preserve ability to investigate/debug micro-interactions on demand.- Near-real-time metrics (minutes) and batch historical analytics.

High-level design:1. Ingestion layer (edge): - Client SDK tags events with type, user_id (hashed), session_id, timestamp, context. - On-device lightweight sampling rules applied for high-frequency micro-interaction types (e.g., hover, tiny animation) with adjustable rates and deterministic sampling by user_id+event_type to keep samples stable. - Critical business events (pageview, add-to-cart, purchase) always sent.

2. Stream processing: - Events routed into a streaming system (Kafka / PubSub). - A stream processor (Flink / Spark Structured Streaming) implements two parallel pipelines: a) Metrics-optimized pipeline: consumes full stream but applies further aggregation & sketching in-flight: - Per-minute aggregates and counts (user-unique counts using HyperLogLog), histograms for latencies, and incremental rollups by key (event_type, product_id, geo). - For sampled micro-interactions, the pipeline applies inverse-probability weighting to produce unbiased estimates for aggregate metrics. - Outputs compact time-series to OLAP store (BigQuery / Redshift / Snowflake) or timeseries DB. b) Raw-event tiering pipeline: writes raw events selectively: - Always persist unsampled critical events. - Persist sampled micro-interaction events to a low-cost object store (S3/ GCS) partitioned by day and event_type. - Maintain a short hot window (e.g., 7–30 days) of raw events in faster storage for debugging; archive older partitions with lifecycle rules to colder tiers (Glacier/Archive). - For heavy-fidelity debugging, keep deterministic seeds + sampling metadata to request on-demand expanded captures (see investigability).

3. Storage & access: - Aggregated metrics (minute/hour/day rollups) in OLAP for dashboards and alerts. - Sampled raw events in object store for investigation; small-size indexed metadata store (Elastic/BigTable) with pointers to partitions for quick retrieval. - Maintain sketches (HLL, Count-Min) for cardinality/frequency queries without full scans.

4. Investigability & on-demand capture: - Provide a "burst capture" feature: when engineers detect anomalies, the system can, for a short period, flip sampling rules (controlled via feature-flag service) to capture a higher fraction or full stream for specific event_types, users, or sessions; stored to raw tier for analysis. - Record sampling rates and seeds in event metadata so analysts can correctly reweight or filter.

5. Data quality and correctness: - Apply inverse-probability weighting to sampled events when computing totals; validate by periodic full-capture A/B runs to measure sampling bias. - Use monitoring dashboards with sampling-rate, ingestion-latency, and key metric deltas; set alerts if sampling changes or weight-corrected metrics drift.

Scalability & cost optimizations:- Push aggregation to stream layer to reduce downstream storage and query cost.- Use deterministic client-side sampling to reduce ingress volume.- Store only aggregated time-series in expensive OLAP; keep raw only in cheap object storage and for limited retention.- Use sketches for high-cardinality metrics to avoid heavy JOINs.

Trade-offs:- Sampling + reweighting introduces variance; mitigated by appropriate sample rates and periodic full-capture validation.- More complex pipeline (dual-path) increases operational complexity but keeps costs low while preserving investigability.

Operational considerations:- Provide tooling for analysts to compute unbiased estimates from sampled data (libraries that apply weights).- Governance: document sampling policies, retention, and privacy/PII handling (hash/PII stripping before storage).- Run periodic audits comparing sampled-derived metrics against occasional full ingestion to recalibrate sampling.

This design balances cost and accuracy by reducing event volume through deterministic sampling and early aggregation while keeping sampled raw data and velocity controls so teams can investigate micro-interaction issues when necessary.

MediumSystem Design

67 practiced

Product requests a funnel dashboard but the product must honor tracking opt-outs (users who have disabled tracking) and support deletion requests. Design a privacy-aware pipeline that respects opt-outs, supports deletions, and still provides useful aggregated insights to product teams. Describe fallback approximations if detailed user-level data is unavailable.

Sample Answer

Requirements & constraints:- Honor user-level tracking opt-outs (no storage/processing of identifiable event data for opted-out users).- Support deletion requests (right to be forgotten) that remove past stored data.- Provide funnel metrics (conversion rates, time between steps) with privacy protections.- Low-latency dashboards with aggregated insights, auditable lineage.

High-level architecture:1. Event ingestion (Kafka / Kinesis) — events include user_id hashed, device_id, tracking_consent flag.2. Consent filter at ingestion/edge: - If consent = false → route only minimal, non-identifying telemetry to a privacy stream. - If consent = true → route to full-event stream.3. Storage: - Full-event stream → data lake (partitioned by day) + user-level analytical store (Delta/BigQuery) with PII removed or pseudonymized. - Privacy stream → aggregated counters & sketches (see below).4. Deletion service: - Deletion requests write to a deletions table (user_id or persistent identifier). A GDPR worker runs streaming jobs that: a) Mark user_id as deleted in index. b) Backfill jobs remove or redact rows in full storage (soft-delete flag then physical purge via TTL). c) Invalidate cached user profiles and remove rows in OLAP tables via tombstones (e.g., delta lake overwrite). - Maintain audit logs and receipts.

Processing & funnel computation:- For consented users: compute user-level funnels in the analytics cluster using pseudonymous IDs; store only aggregated outputs (per cohort/day/hour).- For opted-out users: do NOT store user-level sequences. Instead, update privacy-safe aggregates in real time: - Count-min sketches for per-step counts (approximate counts with bounded error). - HyperLogLog for unique counts. - Differentially private histograms for time-between-steps (add calibrated noise). - Pre-aggregated cohort buckets (e.g., device-type, region) to allow breakdowns without user traces.

Fallback approximations when user-level data unavailable:- Use aggregated-only funnel from privacy stream: step counts from sketches + HyperLogLog uniques to estimate conversion rates.- Apply deterministic re-scaling: combine consented user funnels with estimates from aggregates to extrapolate full population metrics (document assumptions).- Use differential privacy for confidence intervals and to prevent leakage when bucket sizes are small (suppress or coarsen low-count cells).- Provide analysts with uncertainty metrics: sketch error bounds, DP epsilon, and cohort sizes.

Scalability & performance:- Use streaming processing (Flink/Spark Structured Streaming) to update aggregates in real time.- Materialize aggregates into OLAP store (BigQuery/Redshift) for dashboards; refresh cadence depends on SLA.- Partitioning, compaction, and index deletion queues to keep purge operations efficient.

Security & governance:- Key management for hashing/pseudonymization; peppered hashes stored separately.- Role-based access: only aggregated data accessible to product; raw low-level data restricted.- Monitoring: alerts on deletion lag, aggregate anomalies, and privacy budget exhaustion.

Trade-offs:- Differential privacy provides strong guarantees but adds noise; tune epsilon per product needs.- Sketches are memory-efficient but approximate; must communicate error bounds.- Deleting historical granular data increases cost and complexity; use soft-delete + TTL to balance immediacy vs. throughput.

Result: Product gets near real-time funnel dashboards with per-cohort breakdowns and quantified uncertainty, while legal and privacy requirements (opt-outs, deletions) are enforced end-to-end and auditable.

HardSystem Design

67 practiced

The product team wants near-real-time analytics but you must optimize for a limited budget. Propose a cost-optimized cloud architecture (choose AWS, GCP, or Azure) for 1–5 minute dashboard SLAs. Include streaming ingestion, processing framework, storage choices, and how you would balance on-demand queries vs materialized tables.

Sample Answer

Requirements & constraints:- Near-real-time dashboards with 1–5 minute SLA- Minimize recurring cost on limited budget- Support streaming ingestion, transforms, analytical queries for analysts

High-level choice (GCP — cost + BigQuery fit):- Ingest: Cloud Pub/Sub for durable, low-cost streaming- Processing: Dataflow (Apache Beam) for lightweight streaming transforms/ETL- Storage/warehouse: BigQuery for analytics, with GCS as low-cost cold store- Serving: BigQuery + materialized views + BI tool (Looker/Looker Studio)

Architecture:1. Producers -> Pub/Sub topics (partition by logical key)2. Dataflow streaming job(s): - Validate, dedupe (idempotent keys), enrich, window/aggregate small-timescale metrics (e.g., 1-min tumbling) - Buffer micro-batches (e.g., 30–60s) to reduce BigQuery streaming cost - Write: - Aggregates -> BigQuery via batch load (write newline-delimited JSON/AVRO to GCS, then load) every 60s for cost efficiency and table partitions - Raw events -> GCS (append) and occasional load to BigQuery (daily) for historical/ML needs - For ultra-low-latency single-record needs, use BigQuery streaming inserts sparingly

Cost-optimization patterns:- Prefer micro-batch load jobs from GCS to BigQuery over continuous streaming inserts when SLA allows (1–5 min): load jobs are cheaper per GB and avoid streaming insert fees.- Use Dataflow autoscaling with worker min=0 and responsive scaling; use FlexRS or batch workers for non-critical jobs.- Partition and cluster BigQuery tables by date and relevant keys to reduce scanned data and query cost.- Use partition expiration to remove old data and lower storage cost.- Use BigQuery materialized views for frequently accessed aggregations (precompute), which are auto-updated and cheaper than repeated ad-hoc scans.- For ad-hoc/low-frequency analyst queries, rely on on-demand BigQuery (pay-per-query) but enforce quotas and use cost controls (BI engine reservation only if heavy dashboard concurrency).- If steady query volume justifies it, buy a small flat-rate reservation for slots to reduce long-term cost.

Balancing on-demand queries vs materialized tables:- Materialize critical 1–5 minute aggregates used by dashboards (minute-level rollups) as either: - Dataflow writes pre-aggregates to partitioned BigQuery tables (best: micro-batch loads), or - BigQuery materialized views built on recent-partition tables for simple aggregations.- For exploratory ad-hoc analysis, leave raw or daily-loaded tables for on-demand queries; protect budgets with query caps, cost controls, and analyst training (use preview, dry-run).- Use short-window incremental materializations (e.g., last 7 days pre-aggregated) to reduce compute for dashboards while keeping full raw history in cheaper GCS.

Reliability, correctness & ops:- Ensure exactly-once semantics via dedupe keys and Dataflow state.- Monitoring: Cloud Monitoring + alerting on Pub/Sub backlog, Dataflow worker count, BigQuery slot usage and GCS size.- Schema evolution via Avro/Protobuf schemas in Pub/Sub to avoid downstream errors.- Security: IAM least privilege, CMEK for BigQuery/GCS if required.

Trade-offs:- Micro-batch + GCS+load: lower cost, slightly higher latency (seconds) but within 1–5 min SLA — chosen for budget.- Pure streaming inserts: lower latency (sub-second) but higher cost and maintenance; use only for critical single-row needs.

Outcome:- Near-real-time dashboards within 1–5 minutes, controlled cost by favoring micro-batch loads, partitioned/clustered storage, limited streaming inserts, materialized views for hot aggregates, and cost governance for ad-hoc queries.

HardTechnical

69 practiced

You inherit disparate event schemas across iOS, Android, and Web with different field names and inconsistent types. Propose a robust onboarding and best-practices framework to harmonize event naming, typing, and validation across platforms. Describe tooling, linters, CI checks, and how to handle legacy events.

Sample Answer

Approach summary:Create a single-source-of-truth (SSOT) event contract system, enforce it in platform SDKs and CI, provide automated migration/adaptation for legacy events, and monitor for drift. Use schema-first (Avro/Protobuf/JSON Schema) with registry + generated client types and linters to guarantee naming, types, and compatibility.

Framework and onboarding- SSOT: repo "events-specs" with one schema per event (JSON Schema or Avro/Protobuf). Include metadata: owners, stability (alpha/beta/stable), deprecation date.- Onboarding docs + checklist: how to add/modify events, naming conventions (snake_case or camelCase decided), required fields, event-level and field-level descriptions, semantics and units.- Generate typed SDKs: run codegen to produce TypeScript, Kotlin, Swift types and validation helpers so developers use compiled types rather than hand-rolled payloads.

Tooling and linters- Schema registry: Confluent Schema Registry or self-hosted registry for Avro/Protobuf; or store JSON Schema in Git with CI validation.- Linters: platform-specific rules that reference SSOT: - Web: ESLint plugin that enforces use of generated event factories and field names. - iOS: SwiftLint + a custom SwiftLint rule or a build-phase script that forbids raw Dictionary-based event construction; require EventFactory. - Android: Detekt/ktlint + compiler plugin or lint task that enforces generated types.- Pre-commit hooks: run codegen-check, formatters, and lightweight schema validation locally.

CI checks and contract tests- PR validations: - Schema PR must pass schema linter (names, types, semantic tags), compatibility check (backward/forward using registry tools). - Auto-generate SDKs and run platform unit tests that ensure serialization matches schema. - Contract tests: generate canonical payloads and run end-to-end ingestion smoke test against staging pipeline (ingest -> validation -> ETL). - Enforce semantic versioning for schema changes; breaking changes require explicit approval and a migration plan.- Runtime validation: - Client-side: optional lightweight validation in debug builds to catch mistakes early (JSON Schema validation or generated validators). - Ingest-side: strict schema validation in first-stage ingestion to route invalid events to a quarantine topic with metadata for developer feedback.

Handling legacy events- Inventory and mapping: scan event streams to catalog existing event names and shapes; produce a mapping table to SSOT events.- Adapter/translation layer: implement an ingestion adapter that maps/normalizes legacy field names/types to current schema (e.g., string->int conversions, renaming).- Phased deprecation: - Phase 0: Accept legacy and new in ingestion; emit telemetry showing legacy usage by app version. - Phase 1: Warn SDKs/telemetry when legacy events received (include owner contact). - Phase 2: Stop accepting legacy after deprecation date; retain raw archived events for historical queries and run backfill jobs if business requires.- Backfill: for critical schema changes, offer an ETL backfill that transforms historical raw events to the new schema and populates warehouse tables (with provenance columns).

Observability and governance- Metrics: percent events matching SSOT, schema drift rate, volume by schema version, quarantine rate, time-to-fix per owner.- Alerts: spikes in schema errors or new unknown fields should page data engineering and owners.- Data catalog integration: register schemas in the data catalog (e.g., Amundsen, DataHub) with lineage to pipelines and dashboards.- Governance: define clear compatibility rules (additive = allowed; rename/type-change = breaking) and enforce via CI.

Example (JSON Schema snippet)

json

{
  "$id": "com.prod.user.signup",
  "title": "user_signup",
  "type": "object",
  "properties": {
    "user_id": { "type": "string" },
    "created_at": { "type": "string", "format": "date-time" },
    "source": { "type": "string", "enum": ["ios","android","web"] }
  },
  "required": ["user_id","created_at"]
}

Why this works- SSOT + codegen eliminates manual name/type mismatch.- Linters and CI prevent divergent changes before merging.- Adapter + phased deprecation lets you stabilize without losing historical data.- Observability and owner accountability close the loop so schema quality improves over time.

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