Real, paraphrased data engineer interview questions from Meta, Amazon, Apple, Netflix, and Google. Sourced from 287 reported interview loops at FAANG companies in our dataset of 1,042 reports collected 2024 to 2026. Every question includes the company tag, the level it was asked at, and a worked answer with the specific signals interviewers at that company score for. Pair with the our data engineer interview prep hub.
FAANG loops share a structure but differ in emphasis. The table below summarizes the differential focus we've measured across 287 FAANG interview reports.
| Company | Loop Length | Distinctive Emphasis | Common Tools |
|---|---|---|---|
| Meta | 5-6 rounds | Product data sense, graph problems, behavioral depth | Presto, Spark, Hive, Airflow |
| Amazon | 5-7 rounds | Leadership Principles round (high weight), scalable design | Redshift, EMR, Glue, Kinesis, Lambda |
| Apple | 4-6 rounds | Metadata pipelines, privacy-aware design, ML platform | Spark, Cassandra, internal tools |
| Netflix | 5-6 rounds | Streaming systems, operational maturity, keeper test culture round | Kafka, Flink, Spark, Iceberg, Druid |
| 5-7 rounds | BigQuery internals, analytics rigor, theoretical depth | BigQuery, Dataflow, Pub/Sub, Spanner |
Meta's loop emphasizes product-data sense (build the metric for X), graph problems (friend-of-friend), and a heavy behavioral component.
Amazon's bar is the Leadership Principles round (with a Bar Raiser), plus scalable system design with cost awareness.
Apple's loop emphasizes metadata pipelines, privacy-aware design (differential privacy where possible), and ML platform infrastructure.
Netflix's loop emphasizes streaming systems, operational maturity (incident handling), and the keeper-test culture round.
Google's loop leans on BigQuery internals, analytics rigor, and theoretical depth (e.g., why a particular algorithm has a specific complexity).
Across the 287 FAANG loops in our dataset, four patterns appear in nearly every loop regardless of company: a deduplication SQL question (typically with ROW_NUMBER), a rolling-window analytics question, a system design with exactly-once requirements, and a behavioral story about disagreement.
If you have time for only one prep block before a FAANG loop, drill those four patterns until they're reflexive. Then layer in the company-specific patterns from this page. Then open the round-by-round guides: window functions and SQL patterns interviewers test, system design framework for data engineers, behavioral interview prep for Data Engineer.
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