Walk through a data pipeline project

Describe a data pipeline project you built or owned end-to-end.

In your answer, cover:

• The business problem and downstream consumers (dashboards, models, APIs, etc.).
• Data sources and expected volume/velocity (batch vs streaming).
• Architecture choices (e.g., ingestion, storage, transformation, orchestration) and why you chose them.
• Data modeling choices (raw/bronze-silver-gold, dimensional model, etc.).
• Data quality and reliability: validation checks, schema evolution, idempotency/dedup, late-arriving data, backfills.
• Operational concerns: SLAs (latency/freshness), monitoring/alerting, incident handling, cost/performance tradeoffs.
• One key lesson learned and what you would change if you rebuilt it.

Solution

A strong interview answer is structured (STAR) and shows ownership plus concrete engineering/analytics tradeoffs.

1. Situation / Goal

• State the business goal and users: “We needed daily revenue + retention metrics powering exec dashboards and model features.”
• Define SLAs: freshness (e.g., data ready by 9am), latency (e.g., <30 min), correctness (e.g., <0.5% missing events).

2. Data + Constraints

• Sources: app events (Kafka), DB tables (CDC), third-party APIs.
• Constraints: scale, PII handling, regional compliance, schema changes, late events.

3. Architecture (and why)

• Ingestion: batch (Airflow + incremental extracts) or streaming (Kafka/Flink) depending on freshness needs.
• Storage layers: raw landing (immutable), processed (cleaned/dedup), curated marts (business definitions).
• Transformations: SQL/dbt or Spark; justify with team skillset, cost, and data size.
• Orchestration: DAG with retries, backfills, lineage.

4. Correctness & Data Quality

• Idempotency: write partitioned tables; use merge/upsert with natural keys; make jobs re-runnable.
• Deduplication: define event_id/order_id keys; handle at-least-once delivery.
• Late-arriving data: watermarking; reprocess last N days; separate “finalized” vs “provisional” partitions.
• Validation: row count deltas, null checks, referential integrity, distribution drift checks, anomaly detection.
• Schema evolution: contract tests; tolerate additive columns; alert on breaking changes.

5. Metrics definitions & governance

• Define “revenue”, “active user”, “retention” precisely and keep definitions in one place (semantic layer / docs).
• Version changes to definitions; run backfills when logic changes.

6. Operations

• Monitoring: freshness + completeness dashboards; SLA alerts; on-call playbook.
• Performance/cost: partitioning/clustering, incremental models, sampling for dev, caching.
• Incident example: describe detection → triage → mitigation → postmortem.

7. Learning / Iteration

• Example lessons: added data contracts after schema break; introduced incremental + backfill strategy; improved alerting from static thresholds to anomaly-based.
• Show impact: reduced pipeline failures, improved freshness, saved compute cost, increased stakeholder trust.

What interviewers look for: clear requirements, correct handling of real-world data issues (duplicates, late data, backfills), measurable impact, and operational maturity.