Real questions asked in Google data engineering interviews. Covers BigQuery, GCP, SQL, system design, and data pipeline architecture.
Google's data engineering interviews emphasize BigQuery, Dataflow, Pub/Sub, Cloud Storage, and SQL at scale. These questions come from actual Google interview loops and cover technical depth, system design for petabyte-scale data, and best practices for GCP-native data platforms.
This collection contains 6 curated questions: 1 easy, 2 medium, and 3 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are join (3), optimization (3), partition (3), bigquery (2), spark (2), and airflow (1). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
Compare Airflow's @daily vs once trigger scheduling.
Design a daily ETL pipeline to ingest API data into BigQuery.
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?
Handle schema evolution in production.
How do you handle conflict with a product manager?
What excites you about working at Google?
Google's loop emphasizes SQL at scale, BigQuery and the GCP data stack (Dataflow, Pub/Sub, Cloud Storage, Dataproc), data pipeline and system design for petabyte-scale data, and coding (usually Python). Expect strong focus on cost/performance trade-offs, partitioning and clustering, streaming vs batch, and 'Googleyness' behavioral rounds. Depth of reasoning matters more than memorized answers.
BigQuery is serverless and fully managed — there are no clusters to size; it separates storage from compute and bills by data scanned (on-demand) or by slots (capacity). It auto-scales to petabytes, uses columnar storage with a distributed execution engine (Dremel), and needs no index tuning. You optimize via partitioning, clustering, and scanning fewer columns rather than managing hardware.
Avoid SELECT * — scan only needed columns since you pay per byte read. Partition tables by date and cluster by high-cardinality filter columns to prune data. Materialize repeated subqueries, use approximate aggregations (APPROX_COUNT_DISTINCT) where exactness isn't required, and prefer scheduled/materialized views for dashboards. Set the maximum-bytes-billed guardrail to catch runaway queries.
Dataflow is a serverless, auto-scaling service for Apache Beam pipelines that handles both batch and streaming with a unified model — no cluster management. Dataproc is managed Hadoop/Spark where you provision clusters and run existing Spark/Hive jobs. Choose Dataflow for new streaming-first pipelines and hands-off scaling; choose Dataproc to lift-and-shift existing Spark/Hadoop workloads.
Pub/Sub is Google's global, serverless messaging service for decoupling producers from consumers. It ingests high-throughput event streams (clicks, IoT, logs), buffers them durably, and fans them out to subscribers. A typical streaming pattern is Pub/Sub → Dataflow → BigQuery: Pub/Sub absorbs spikes, Dataflow transforms in real time, and BigQuery serves analytics.
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