Question 1

Tell me about a data pipeline you built from scratch. What were the trade-offs?

Accepted Answer

Walk through the requirements first — data volume, freshness SLA, consumers — then the architecture choices and what you traded away: batch simplicity vs. streaming freshness, managed services vs. cost, schema flexibility vs. query performance. Ending with what you'd change today shows the design was deliberate, not cargo-culted.

Question 2

Describe a time a pipeline failure caused a downstream business impact. What did you do?

Accepted Answer

Use an incident structure: how you learned of it (ideally your alert, not an angry analyst), the blast-radius assessment, the communication to downstream consumers, and the fix. The differentiator is prevention — the data-quality check, SLA monitor, or backfill runbook that exists now because of that failure.

Question 3

How do you handle data quality issues at scale?

Accepted Answer

Describe testing at the boundaries: schema validation and freshness/volume/null checks at ingestion, contract tests with producers, and monitoring that catches drift before dashboards break. Name a real tool or framework you've used (dbt tests, Great Expectations) and emphasize alerting the humans who own the source, not silently patching bad data.

Question 4

Walk me through how you've evolved a data model as business needs changed.

Accepted Answer

Show evolution without breaking consumers: versioned models, deprecation windows, expand-then-contract migrations, and talking to downstream users before dropping anything. A story where the business pivoted and your model absorbed it with modest rework demonstrates you design for change.

Question 5

Describe your experience working with analysts and data scientists as customers.

Accepted Answer

Frame analysts and scientists as customers whose productivity is your product: describe how you gathered their pain points (slow queries, untrustworthy tables), what you changed (documented marts, self-serve models, faster iteration), and how you measured improvement — time-to-insight, adoption, fewer ad-hoc requests to your team.

Question 6

Design a pipeline to ingest event data at 100K events/second.

Accepted Answer

Sketch the standard shape and justify each piece: a durable log (Kafka/Kinesis) to absorb bursts and decouple producers from consumers, partitioning by a key that balances load, stream processing for validation/enrichment, and landing in cheap object storage plus a warehouse. Cover the reliability details that show experience: exactly-once vs. at-least-once semantics, dead-letter queues, and backpressure.

Question 7

Explain the trade-offs between star schema and data vault modeling.

Accepted Answer

Star schema optimizes for query simplicity and BI-tool compatibility — facts plus conformed dimensions, easy for analysts. Data vault optimizes for auditability and absorbing source-system change — hubs/links/satellites, but nobody queries it directly without a presentation layer. The pragmatic answer most interviewers want: star schema for the mart layer, vault only when you have many volatile sources and compliance needs.

Question 8

How do you handle late-arriving data in a streaming pipeline?

Accepted Answer

Cover the standard toolkit: event-time processing with watermarks (not processing-time), windowing with allowed lateness, and a correction path — either reprocessing windows or emitting updates downstream consumers can merge. The key trade-off to name: how long you wait balances completeness against freshness, and the business SLA decides it.

Question 9

Walk me through how you'd detect and alert on data quality regressions.

Accepted Answer

Describe layered checks: schema and type validation at ingestion, statistical checks (volume, null rates, distributions) against historical baselines, and business-rule assertions (revenue can't be negative) at the mart layer. Alert on anomalies before consumers see them, route alerts to source owners, and track incidents to find chronic offenders.

Question 10

How do you decide between batch and streaming for a new use case?

Accepted Answer

Give a decision rule, not a preference: what freshness does the consumer actually need, and what does the 10x-simpler batch version cost in staleness? Most 'real-time' requests survive scrutiny as hourly batch; genuine streaming needs (fraud, operational alerting) justify the operational overhead. Saying 'I'd start batch and promote to streaming when the SLA demands it' signals maturity.

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