Google Data Engineer Interview Questions

I would give a concise story about how I moved toward building reliable data systems, then connect that path to the kind of work a data engineer actually does. A strong answer shows that I care about SQL, pipelines, data quality, and making analysis trustworthy at scale rather than just moving data around.

I would connect my answer to the scale of the data problems, the quality bar, and the chance to build systems that affect many products and teams. A good response should show that I am excited by large analytical platforms, strong engineering culture, and the need to make data reliable for decision-making.

I would first check partition pruning, clustering, filter placement, and whether the query is selecting unnecessary columns. Then I would inspect the execution details to see where the cost is coming from and rewrite the query so BigQuery can eliminate more data early. In practice, the cheapest optimization is usually better query shape, not more compute.

Partitioning splits a table into coarse segments, often by date or timestamp, so queries can skip irrelevant partitions. Clustering organizes data within those partitions based on one or more columns to improve pruning further. I would use partitioning for the biggest coarse-grained reduction and clustering when queries repeatedly filter on additional columns.

I would define a clear ingestion watermark, track late-arriving data, and make the pipeline idempotent so reruns are safe. I would land raw events first, validate them, then build downstream transformations from the staged data rather than from direct source reads every time. That makes the pipeline easier to recover and easier to audit.