Real FAANG Questions
FAANG Data Engineer Interview Questions
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.
The Short Answer
FAANG loops are rigorous but not magical. Each company has its own flavor: Meta loves graph and product-data questions, Amazon's bar is Leadership Principles + scalable system design, Apple cares about metadata pipelines, Netflix tests streaming and operational maturity, Google leans on BigQuery internals and analytics rigor. Drill the questions below organized by company. After this, open the company-specific guide for the loop you're targeting.
Updated April 2026·By The DataDriven Team
How FAANG Loops Differ From Other Companies
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 Data Engineer Questions
Meta's loop emphasizes product-data sense (build the metric for X), graph problems (friend-of-friend), and a heavy behavioral component.
L4 · SQL
Calculate DAU and 7-day rolling DAU
DISTINCT user_id per day = DAU. Rolling: COUNT(DISTINCT user_id) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) does not work because DISTINCT in window is not supported. Self-join trick: for each day d, count distinct users in (d-6, d).
L4 · Product Data
Define and compute 'engaged user' for a feed product
Operational definition first: e.g., 'user who scrolled 5+ posts and clicked 1+ in a 24h window'. Then SQL with sessionization. Discuss why this definition vs alternatives (likes, time-spent, return-visits).
L5 · SQL
Friend-of-friend graph traversal in SQL
Recursive CTE if degree is bounded. WITH RECURSIVE friends_of: base = direct friends, recurse to depth N. Or: self-join friendship table N times for fixed depth. Discuss why graph databases beat SQL for unbounded depth.
L5 · System Design
Design a notification deduplication system at 1B events/day
Kafka -> Flink with keyed state by (user_id, content_id), 24h TTL. If state hit: drop. Else: emit notification, write state. Cover state size estimate, Redis vs Flink-managed state trade-off.
L5 · Behavioral
Tell me about a time you handled ambiguity (Meta-style)
Meta's behavioral round emphasizes 'move fast and break things' culture awareness. Story should show you committed before having full information, with a measurable outcome and a learned lesson.
Amazon Data Engineer Questions
Amazon's bar is the Leadership Principles round (with a Bar Raiser), plus scalable system design with cost awareness.
L4 · SQL
Top product per category by quarterly revenue
DENSE_RANK PARTITION BY category, quarter ORDER BY rev DESC. Filter rk = 1. Discuss why DENSE_RANK over RANK or ROW_NUMBER. Edge case: ties.
L5 · System Design
Design an order processing pipeline for Amazon scale (1M orders/min peak)
Kinesis (sharded by customer_id mod N) -> Lambda for enrichment -> DynamoDB for order state -> async Glue ETL to Redshift for analytics. Cover hot key on Black Friday, idempotency for retry, audit trail for chargebacks.
L5 · System Design
Design a recommendation pipeline cost-optimized for AWS
Daily Glue job from S3 historical -> SageMaker training -> features pushed to DynamoDB. Real-time scoring via Lambda + DynamoDB lookup. Discuss S3 storage class transitions, DynamoDB on-demand vs provisioned, Glue worker types.
L5 · Leadership Principles
Tell me about a time you had to deliver results (Amazon LP)
Map to Deliver Results LP. Specific number outcome. Single decision you owned. End with what you would do differently. Bar Raiser specifically grades the postmortem.
L5 · Leadership Principles
Tell me about a time you took a calculated risk (Bias for Action)
Specific situation where you committed without full data. What you bought (speed) vs what you risked (correctness). Quantify both. Show post-decision review.
L6 · System Design
Design a multi-region active-active warehouse for Amazon Retail analytics
Region-local writes to Redshift Serverless, async cross-region replication via S3 intermediary. Conflict resolution: last-writer-wins for events, CRDT-style for counters. SLA tiering. Cost: 2x storage, complex consistency.
Apple Data Engineer Questions
Apple's loop emphasizes metadata pipelines, privacy-aware design (differential privacy where possible), and ML platform infrastructure.
L4 · SQL
Find duplicate metadata records across regional data centers
GROUP BY composite key, HAVING COUNT > 1. Apple-specific: discuss why metadata duplicates cause user-visible bugs (e.g., duplicate Photos albums) and the reconciliation pipeline approach.
L5 · Modeling
Design a privacy-preserving analytics schema for App Store telemetry
Differential privacy at ingest (Laplace noise on counts). User-level aggregates only after k-anonymity threshold. Cohort tables for trend analysis. Avoid raw user_id in analytics tables; use rotating salted hash.
L5 · System Design
Design a metadata ingestion pipeline for media files at iCloud scale
Kafka per region -> Flink for enrichment (face detection, EXIF parsing) -> Cassandra for live serving + S3 + Iceberg for analytics. Cover schema evolution as new metadata fields are added quarterly.
L5 · System Design
Design an A/B test analysis pipeline that respects user privacy
Exposure log + outcome log, joined by salted user_id at compute time. Aggregate to experiment_id + variant grain. Statistical significance computed downstream. Discuss why raw user-level results are never persisted.
Netflix Data Engineer Questions
Netflix's loop emphasizes streaming systems, operational maturity (incident handling), and the keeper-test culture round.
L4 · SQL
Compute video session duration with handling for app close vs background
Sessionize playback events with 5-minute gap. Distinguish 'paused' (gap < 5 min) from 'ended' (gap >= 5 min OR explicit end event). Discuss tradeoff: counting paused vs. completed differently.
L5 · System Design
Design Netflix's playback events pipeline (300K events/sec global)
Kafka per region -> Flink stateful keyed by user_id + content_id -> Iceberg on S3 (event-time partitioned) -> Druid for real-time dashboards + Spark daily to Snowflake equivalents. Cover regional failure mode.
L5 · System Design
Design A/B testing infra for content recommendations
Exposure assignment service (deterministic hash on user_id) -> exposure log to Kafka -> daily Spark aggregation -> stats engine. Cover the new-user cold-start problem and the 'experiment within experiment' nesting.
L5 · Behavioral
Netflix keeper test: tell me about a time you proactively eliminated work
Specific story where you killed a project, deprecated a system, or removed a process. Quantify the impact (engineer-hours freed, infra cost saved). Show that you proposed it; don't claim it was assigned.
L5 · Behavioral
Tell me about a time you disagreed with your manager
Netflix's culture values dissent. Story should show specific disagreement, how you escalated it via data, what you did when the decision went against you. 'Disagree and commit' framing is right.
Google Data Engineer Questions
Google's loop leans on BigQuery internals, analytics rigor, and theoretical depth (e.g., why a particular algorithm has a specific complexity).
L4 · SQL (BigQuery)
Use ARRAY_AGG and UNNEST for nested data analysis
Common in Google's BigQuery-heavy loops. SELECT user_id, ARRAY_AGG(STRUCT(event_type, ts) ORDER BY ts) FROM events GROUP BY user_id. Explain when this beats joins for analytical workloads.
L5 · BigQuery
Why does this BigQuery query cost $50 instead of $5?
Common Google interview pattern: candidate is shown a query and bill. Identify: SELECT * (scans all columns), no WHERE on partition column (full table scan), JOIN on hashed column (shuffle). Fix via column pruning, partition predicate, broadcast join hint.
L5 · System Design
Design a search-query analytics pipeline at Google scale
Pub/Sub -> Dataflow streaming -> BigQuery streaming inserts (clustered by date and query_hash). Daily Dataflow batch for aggregations -> separate BigQuery tables for trends. Discuss why streaming inserts are billed differently than batch loads.
L5 · Theoretical
Compare HyperLogLog to Count-Min Sketch for unique-user counting
HLL: estimate cardinality with constant memory, ~2% error. CMS: estimate frequency of items, with a chosen error bound. Different problems. Discuss when each is right. BigQuery uses HLL++ for APPROX_COUNT_DISTINCT.
Cross-FAANG Patterns
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.
Data engineer interview prep FAQ
Are these the actual interview questions FAANG companies ask?+
These are paraphrased and de-identified versions of questions reported by candidates in our dataset. Direct quotes from copyrighted question banks are not included. The patterns and signals are accurate.
How do FAANG loops compare in difficulty?+
Amazon and Meta loops are typically the longest (5-7 rounds). Netflix is the most opinionated culturally (keeper test). Apple has the highest variance by team. Google has the most theoretical depth in some teams. None is uniformly harder; the bar at L5 is similar across all five.
Should I focus on one FAANG company or prep broadly?+
Prep broadly first (the universal patterns), then specialize for the loop you have scheduled. The company-specific tactics in this page give you the last 10% that differentiates a strong candidate.
How important are the Leadership Principles at Amazon?+
Critical. The LP-only round (sometimes called Bar Raiser) is graded as heavily as any technical round. Map your 12 behavioral stories to the 16 LPs. Know which story serves which LP.
Does Netflix really do the keeper test in the interview?+
Not literally, but the culture round explicitly probes whether you would be 'kept' by a hypothetical manager. Stories about proactively eliminating work, dissenting publicly, and operating with ambiguity are the right material.
What if I'm interviewing for FAANG but the team is non-standard (e.g., Meta Reality Labs)?+
The base loop structure is consistent across teams. The questions skew toward the team's domain. For Reality Labs: expect spatial data, low-latency telemetry, ML feature pipelines. The patterns from this page still apply; the example data changes.
How do I get a FAANG interview in the first place?+
Three primary paths: referrals (highest hit rate), direct application via career sites (moderate), recruiter outreach to your LinkedIn (passive but real). Polished LinkedIn + GitHub portfolio + 3+ years of relevant experience is the typical baseline.
Practice the FAANG Mock Interview
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