More in Analytics & Metrics — page 6
What is a data schema and why enforce it at ingestion?
Tests schemas as contracts and ingestion validation as a quality gate. Strong answers cite blueprints with constraints, fail-fast ingestion catching type errors upstream, and downstream trust. Red flag: treating schemas as optional docs affecting only storage.
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?
WHAT IT TESTS: Judgment between dropping rows, imputing, or flagging gaps for dashboards. ANSWER OUTLINE: Check MCAR/MAR/MNAR; prefer median or mean imputation; add an "is_missing" indicator. RED FLAG: Row deletion or zero-fill without bias checks.
Design a data quality framework from source to consumption
This tests full-lifecycle data architecture. Strong answers define ownership first, then schema contracts at ingestion, profiling and anomaly detection in CI/CD, column-level lineage, and KPI-linked scorecards. Red flag: tools before ownership or RACI.
Diagnose out-of-memory errors in a growing Spark job
WHAT IT TESTS: Spark SQL tuning and OOM diagnosis. ANSWER OUTLINE: Inspect plans for skewed partitions and joins; tune shuffle partitions, batch sizes, and join strategies; leverage AQE and statistics.
What is data partitioning in a cloud data warehouse?
Tests physical data layout and cost/performance tradeoffs. Strong answers define time-based or integer-range partitioning, explain partition pruning avoids full scans, and warn against high-cardinality keys.
How would you handle late-arriving data in a streaming analytics pipeline?
Tests understanding of event-time processing, watermarks, and windowing for correctness. A strong answer covers watermarks with allowed lateness, event-time triggers, and idempotent updates to sinks.
Describe star and snowflake schemas and their trade-offs
WHAT IT TESTS: Dimensional modeling denormalization trade-offs. ANSWER OUTLINE: Star schemas flatten dimensions for fast joins; snowflake schemas normalize hierarchies to reduce redundancy but add joins.
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.
Outline an automated pipeline to load a daily CSV into a database
WHAT IT TESTS: designing a minimal production-ready ingestion pipeline, not just listing tools. ANSWER OUTLINE: event trigger on drop, schema validation, idempotent load, and observability.
Differences between ETL and ELT, and when to choose each
WHAT IT TESTS: Pipeline architecture tradeoffs. ANSWER OUTLINE: ETL transforms before loading for structured data; ELT loads raw first and transforms in the warehouse for scale. RED FLAG: Calling one better without citing volume, structure, or compute.
How do you guarantee at-least-once event delivery for a financial transaction?
WHAT IT TESTS: Atomicity of state changes and side effects without 2PC. ANSWER OUTLINE: Write events to a DB outbox in the same transaction as the biz update; a relay polls and publishes to analytics. RED FLAG: 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.
How do you approach user identity stitching across devices?
Tests cross-channel identity resolution design. Strong answers capture a persistent anonymous ID per device, rekey to a person ID at login via field-based or graph-based stitching, and respect consent.
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?
WHAT IT TESTS: Linking platform spend to business value and team health. ANSWER OUTLINE: Cite adoption, time to insight, downtime cost, and cost per workload; then describe cost tags and usage telemetry.
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.