# FAANG Data Engineer Interview Questions > FAANG-tagged data engineer interview questions with per-company rubrics. Canonical URL: Breadcrumb: [Home](https://datadriven.io/) > [FAANG](https://datadriven.io/faang-data-engineer-interview-questions) ## Summary FAANG data engineer interview questions tagged from reported Meta, Amazon, Google, Netflix, and Apple loops. Each company's rubric is different. Meta weights communication. Amazon weights Leadership Principles. Google weights complexity reasoning. Netflix weights Spark. Apple weights warehouse architecture. The catalog is filterable down to a single company once you know your target. ## What this page covers FAANG (Meta, Amazon, Apple, Netflix, Google) data engineer interview loops share a common high-level structure (SQL, Python, data modeling, system design, behavioral) but the rubric weights and the specific test focuses differ significantly per company. A data engineer candidate who optimizes for one FAANG without understanding the others' specific bars often clears one loop and fails the next. Meta (formerly Facebook) weights communication and trade-off articulation heavily. The SQL round tilts toward window functions and gap-and-island patterns for engagement streak detection. Presto and Trino dialect is what you see internally. Design rounds frequently center on the ads attribution pipeline (impressions, clicks, conversions with 28-day windows, multi-touch attribution) or the feed-ranking signals pipeline (10B+ events per day, single-digit-millisecond serving latency for the ML ranker). The behavioral round at Meta explicitly scores 'thinks out loud' and 'asks clarifying questions' as separate dimensions from technical correctness. Amazon weights correctness and clean code heavily in the technical rounds, with Leadership Principles framing every behavioral and design answer. The stack is AWS-native: Redshift, Glue, EMR, Kinesis, S3, Athena. The bar-raiser round (an outside interviewer whose vote can veto a hire) is unique to Amazon and probes deeper on cultural fit. The data engineer SQL round tests Redshift-specific dialect (DISTKEY, SORTKEY, COPY, VACUUM). Google weights complexity reasoning in the Python round more than other data engineer loops; Big-O articulation expected for every data structure choice. The stack is GCP-native: BigQuery, Dataflow, Pub/Sub, Dataproc. BigQuery's QUALIFY and ARRAY/STRUCT manipulation come up in the SQL round. Design rounds expect a GCP-native architecture with cost reasoning (BigQuery slot consumption, Dataflow worker hours). Netflix runs Spark at extreme scale; the PySpark or Scala-Spark round is dedicated (45-60 minutes) and the SQL round includes Spark SQL questions. Iceberg as the table format is internal default; mention it in design rounds. Late-arriving data is a recurring theme; clients can be offline for days and events need to update past aggregates without overwriting. Apple's data engineer interview loop varies more by team than the other FAANGs. The services and analytics teams tilt toward warehouse architecture (currently a mix of Hadoop/Hive legacy and modern Snowflake/Spark). The hardware teams tilt toward operational pipelines feeding manufacturing analytics. The bar is consistently high but the tested topics shift by team. ## Frequently asked questions ### How do FAANG data engineer loops differ from each other? Shared high-level structure (5 rounds: SQL, Python, modeling, design, behavioral) but different rubric weights. Meta weights communication. Amazon weights Leadership Principles and correctness. Google weights complexity reasoning in Python. Netflix weights Spark and late-arriving-data design. Apple varies by team. The dialect and stack assumed in interviews also differs per company. ### Should I prep for all 5 FAANG at once or one at a time? One at a time once you know your target. The foundational skills (SQL, Python, modeling) are 80 percent overlap; the per-company specifics (Presto syntax for Meta, BigQuery for Google, Leadership Principles for Amazon, Iceberg for Netflix) are the last-mile 20 percent that makes the difference between a strong loop and a no-hire. If you are early in prep, build the foundation; if you are 2-4 weeks from an onsite, focus on the specific company's bar. ### Which FAANG has the highest data engineer bar? Subjective and varies by team. Common takes: Netflix has the highest bar for Spark and large-scale design; Google has the highest bar for Python complexity reasoning; Meta has the highest bar for narration and communication; Amazon has the highest bar for cultural fit (via Leadership Principles and the bar-raiser round); Apple varies most by team. None is easy; all expect a candidate who can clear all 5 rounds. ### What is the FAANG-versus-non-FAANG difference for data engineer interviews? FAANG loops are typically more rigorous on scale (problems framed at 10B+ events per day, multi-region complexity), more rigorous on edge cases (multi-seed-grader-style fishing for NULL bugs, tie handling, idempotency), and more rigorous on behavioral rounds (specific cultural frameworks at Meta and Amazon, conversational depth at Netflix). Non-FAANG loops at strong companies (Stripe, Databricks, Snowflake, Airbnb, Uber) often have comparable technical bars but different cultural framings. ### What stack should I assume in data engineer design rounds at each FAANG? Meta: Presto/Trino plus Hive plus Spark plus internal tools. Amazon: AWS-native (Kinesis, Glue, EMR, S3, Athena, Redshift, DynamoDB). Google: GCP-native (Pub/Sub, Dataflow, BigQuery, Dataproc, Cloud Storage, Bigtable). Netflix: Spark plus Iceberg plus Mantis plus Kafka plus S3. Apple: varies by team; Snowflake and Spark common in newer teams, Hadoop/Hive legacy in older ones. ### How do I handle the cultural fit rounds at FAANG? Amazon: 5-7 STAR-D stories explicitly mapped to 2-3 Leadership Principles each. Meta: emphasis on communication and asking clarifying questions; less rigid framework. Google: Googleyness and Leadership themes (ownership, collaboration, ambiguity tolerance) without rigid framework. Netflix: 'stunning colleagues' bar; expect probing for ownership and the ability to disagree productively. Apple: varies; generally expect direct, technical, no-fluff answers. ### What level should I target if I have 5 years of experience as a data engineer? 5 years typically maps to L5/E5/Senior across FAANG. The L5 bar weights trade-off articulation, failure-mode naming (3 per component in design rounds), and mid-round adapt-on-fly. L4 (4-5 years floor) emphasizes clean foundational solutions; L6 (8-10+ years) emphasizes org-level design influence. Recruiters can sometimes flex you between L4 and L5 based on loop performance. ### What is the typical FAANG data engineer onsite duration? 4-5 hours for an onsite, 5-6 hours for senior+. 4-5 rounds of 45-60 minutes each, lunch interview (informal but observed), and sometimes a follow-up call for any unclear signals. Phone screen is typically 60-75 minutes covering SQL plus a behavioral or shortened design. Whole loop process: 4-8 weeks from first phone screen to offer, depending on company and team availability. ## FAANG Data Engineer Interview Preparation Self-paced practice across the 5 FAANG data engineer interview loops, with per-company rubrics for Meta, Amazon, Google, Netflix, and Apple. Provided by DataDriven. ## Related practice catalogs - [Meta data engineer interview questions](https://datadriven.io/meta-data-engineer-interview-questions): Window-heavy SQL on Presto, communication rubric, ads/feed-ranking design. - [Amazon data engineer interview questions](https://datadriven.io/amazon-data-engineer-interview-questions): AWS-native design, Leadership Principles framing, bar-raiser. - [Google data engineer interview questions](https://datadriven.io/google-data-engineer-interview-questions): BigQuery dialect, algorithm-adjacent Python, GCP design. - [Netflix data engineer interview questions](https://datadriven.io/netflix-data-engineer-interview-questions): Spark-heavy with Iceberg, late-arriving data, streaming. - [Full data engineer interview catalog](https://datadriven.io/data-engineer-interview-questions): 1,400+ problems across all 5 rounds. - [Senior data engineer problems](https://datadriven.io/senior-data-engineer-interview-questions): L5+ rubrics across FAANG with trade-off articulation. - [FAANG data engineer mock interview](https://datadriven.io/mock-interview/faang): AI mock with per-company rubric and prompt pool. - [System design across FAANG](https://datadriven.io/system-design-interview-prep): End-to-end design with company-specific architecture preferences. - [Advanced SQL for FAANG L5+](https://datadriven.io/advanced-sql-interview-questions): The 7 advanced patterns tested across all FAANG senior loops. --- Source: DataDriven (https://datadriven.io). 100% free data engineering interview prep. Live code execution against Postgres 16, Python 3.11, and Spark sandboxes. No paywall, no premium tier, no signup gate.