Question 1

Compare Airflow's @daily vs once trigger scheduling.

Accepted Answer

**Architectural Logic**: @daily vs once represent fundamentally different scheduling paradigms with distinct cost and operational implications. **@daily (Schedule Interval)**: DAG runs at fixed cron intervals—each execution processes a discrete schedule interval (e.g., midnight-to-midnight). Produces deterministic runs; catchup can trigger historical backfills (cost explosion if catchup=True on long-running DAGs). **Once**: Single execution—manual trigger or one-off; no recurrence....

Question 2

Design a daily ETL pipeline to ingest API data into BigQuery.

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 3

Design a real-time data pipeline for clickstream events. How to ensure fault tolerance? Where to implement deduplication logic? How to efficiently store 1 billion+ rows?

Accepted Answer

**Section 1 — The Context (The 'Why')**
Real-time clickstream pipelines must handle millions of events per second with fault tolerance. The primary challenge is exactly-once semantics, out-of-order events, and recovering without data loss....

Question 4

Handle midstream schema changes gracefully.

Accepted Answer

**Strategy**: (1) Schema evolution—additive changes; new columns, defaults for old; (2) Schema Registry—versioned, backward/forward compatible; (3) Multiple readers—support versions during transition; (4) Dead-letter for incompatible.

**Parquet/Delta**: mergeSchema=True. Avro: Schema Registry. Design for evolution; document breaking vs additive; compatibility tests in CI....

Question 5

Handle schema evolution in production.

Accepted Answer

**Section 1 — The Context (The 'Why')**
Schema evolution in production requires backward compatibility and coordinated rollout. The primary challenge is adding columns or deprecating without breaking consumers....

Question 6

How do you handle conflict with a product manager?

Accepted Answer

Situation: A PM pushed for a new analytics feature requiring schema changes that would break 20 downstream pipelines and delay a critical migration by 3 months. Task: Resolve the conflict without damaging the relationship or blocking business value. Action: I scheduled a 1:1 to understand their goals—they needed customer cohort analysis for a Q4 launch....

Question 7

Process a large log file (in GBs) to identify the top 10 users by event frequency. Optimize for memory efficiency and handle streaming input.

Accepted Answer

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Process large log file for top 10 users: Use Spark: `spark.read.text(path).select(split(col('value'),' ').getItem(0).alias('user')).groupBy('user').count().orderBy(desc('count')).limit(10)`. For streaming: `readStream` with watermark. Optimize: partition by date; use reduceByKey equivalent; avoid collect....

Question 8

Share a situation where you took ownership of a failing project.

Accepted Answer

Situation: ETL pipeline failing due to source schema change. Approach: diagnosed root cause, communicated to stakeholders, implemented schema evolution (add column with default), added validation layer, documented. Result: pipeline restored within 24h, zero data loss, added monitoring to prevent recurrence....

Question 9

What actions did you take when a deadline was missed due to code errors?

Accepted Answer

Situation: Deadline missed due to bugs. Task: Fix and prevent. Action: I apologized; provided revised timeline. Root cause analysis; fixed issues; added tests. Communicated transparently. Proposed process improvements (integration tests, review checklist). Documented incident. Reflected on gaps....

Question 10

What excites you about working at Google?

Accepted Answer

Research Google. 'Scale—billions, petabytes. BigQuery, Spanner, TensorFlow—world-class infra. Innovation culture; leading engineers; global impact. Contribute to systems serving billions; learn from best.' Scale, culture, impact.

Google Data Engineer Interview Questions

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Google Data Engineer Interview Questions

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