Analytics
494 bites tagged Analytics — interview questions with model answers, and 60-second explainers.
What validation checks would you implement for a user-submitted email field?
Tests whether you separate syntax validation from deliverability and know practical ingestion guards. A strong answer covers RFC-aware syntax, domain checks, normalization, and deduplication.
How do you handle 10% nulls in a key numerical column?
Check MCAR/MAR/MNAR; prefer median or mean imputation; add an "is_missing" indicator. Judgment between dropping rows, imputing, or flagging gaps for dashboards. Row deletion or zero-fill without bias checks.
Describe star and snowflake schemas and their trade-offs
Star schemas flatten dimensions for fast joins; snowflake schemas normalize hierarchies to reduce redundancy but add joins. Dimensional modeling denormalization trade-offs.
Explain data warehouse purpose and how it differs from OLTP
This tests whether you know the OLTP versus analytics split. A great answer contrasts OLTP row-level writes and normalized schemas with warehouse denormalized schemas and BI reads. A red flag is calling a warehouse just a bigger OLTP database.
Differences between ETL and ELT, and when to choose each
ETL transforms before loading for structured data; ELT loads raw first and transforms in the warehouse for scale. Pipeline architecture tradeoffs. Calling one better without citing volume, structure, or compute.
How do you guarantee at-least-once event delivery for a financial transaction?
Write events to a DB outbox in the same transaction as the biz update; a relay polls and publishes to analytics. Atomicity of state changes and side effects without 2PC. Suggesting direct HTTP POSTs or dual writes.
Design client-side event batching and prevent unload data loss
It tests balancing network efficiency and data reliability in browser analytics. Strong answers cover in-memory batching with size or time triggers, sendBeacon or fetch keepalive on visibilitychange, and a retry queue.
Trade-offs: third-party analytics SDK versus in-house pipeline
This tests strategic build-versus-buy judgment for data infrastructure. Strong answers weigh time-to-market, maintenance burden, data sovereignty, and compliance against core product focus.
Conversion metric dropped suddenly with no recent deployments; debug instrumentation causes
Distinguishing real regressions from telemetry pipeline failures. Segment by device, channel, and geography to spot uniform loss signaling a tagging break; verify vendor delays and sampling; check for consent or ad-blocker shifts.
How do you track page views in a Single Page Application?
This tests SPA analytics beyond classic page loads. A strong answer covers History API pushState and popstate events, framework router hooks like useEffect or afterEach, and beaconing views. A red flag is relying only on window.load or polling URL changes.
How do you measure data platform ROI and track it?
Cite adoption, time to insight, downtime cost, and cost per workload; then describe cost tags and usage telemetry. Linking platform spend to business value and team health.
How would you develop balanced KPIs for a two-sided marketplace?
Tests dual-sided metric design beyond B2C playbooks. Strong answers define buyer and seller liquidity separately, prioritize match rate over GMV, and monitor supply-demand balance granularly.
How do you diagnose why a new feature's adoption is flat?
Tests structured analytics thinking across the adoption funnel. A strong answer maps discovery to habituation, segments cohorts, pairs behavior with feedback, and validates via experiments. Red flag: blaming UI without proving users know the feature exists.
Pitfalls of using conversion rate as a checkout North Star?
Tests if you know over-optimizing conversion can degrade revenue quality or trust. Strong answers cite lower AOV or fraud risks, then list guardrails like refund rate, lifetime value, and checkout errors. Red flag: insisting conversion is the sole metric.
How do you translate increase user engagement into a technical measurement plan?
Align with PM to define engagement, map touchpoints for events, pick a north star and guardrails, then draft technical schema. turning vague goals into metrics.
PII: Data That Identifies a Real Person
PII is any data that can identify a real person. Email addresses, IP addresses, and device IDs all count, so analytics systems must mask or hash them before storage. A leaked salt can still expose a hashed email, so do not assume hashing removes PII.
Define and calculate Weekly Active Users (WAU) for Slack
Tests translating a business metric to a technical spec. Define 'active' by key actions (sending messages, not just opening), then `COUNT(DISTINCT user_id)` on an events table, filtering out bots and background syncs. A red flag is a generic definition.
When is an A/B test not feasible, and what is DiD?
This tests your grasp of causal inference when randomization isn't possible. Explain a scenario like a state-level launch, introduce Difference-in-Differences (DiD), and state its core parallel trends assumption.
Design a Schema Validation System for Analytics Events
Tests your grasp of data quality engineering, client/server trade-offs, and failure design. A good answer defines a Tracking Plan, enforces it on both client and server, and handles failures by blocking or forwarding with violation flags.
Calculate Monthly Recurring Revenue (MRR) with SQL
This tests your ability to translate a business metric into a robust SQL query, handling time-series logic. A good answer filters for active subscriptions, sums the price, and correctly amortizes annual plans. A red flag is using incorrect date filtering.
How do you handle timezones for a global daily sales report?
This tests your ability to translate ambiguous business needs (a "day") into a robust data model. First, clarify the business definition of a day. Then, store all event times in UTC and convert to the target timezone at query time for reporting.
Propose a multi-touch attribution model and its data pipeline
This tests your ability to choose a practical data model under real-world constraints. Propose a time-decay or position-based model, then describe the data pipeline: event collection, identity resolution, and aggregation. A red flag is ignoring signal loss.
How would you find leading indicators for long-term churn?
Tests your ability to connect a lagging business KPI to leading product metrics. A good answer defines churned/retained cohorts, analyzes first 30-day engagement differences (e.g., feature adoption), and validates findings. A red flag is jumping to ML models.
Calculate Daily Active Users (DAU) with SQL
This tests your ability to translate a business metric into a precise technical definition and query. A good answer defines "active," specifies the event data needed (user_id, timestamp, event_name), and uses COUNT(DISTINCT user_id).
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