Data Engineering Interview Questions at FAANG 2026

FAANG data engineering interviews are multi-round processes that test technical depth, system design, and coding. Understanding what each company focuses on dramatically changes how you should prepare.

Amazon Data Engineering Interview

Amazon interviews heavily focus on the Leadership Principles — every question, even technical ones, is framed around them. Technical rounds typically cover: • Large-scale ETL pipeline design on AWS (Glue, EMR, Redshift, S3) • PySpark questions with a business framing ("find top sellers per region per month") • SQL window functions and aggregations • System design: "Design a real-time recommendation pipeline" Common question: "You have a 10TB daily events table in S3. Design a pipeline to compute daily active users per country, available by 6am."

Meta (Facebook) Data Engineering Interview

Meta focuses heavily on data modelling, SQL, and Spark. They often give you a scenario involving user behaviour data. • SQL window functions (LAG/LEAD for user retention, session analysis) • PySpark transformations on user event data • Data modelling: "How would you model the Friend graph for analytics?" • Hive/Presto query optimisation Common question: "Write a query to find users who were active on at least 3 consecutive days last month."

Google Data Engineering Interview

Google (Alphabet) tests coding quality and algorithmic thinking more than most FAANG companies. • BigQuery SQL — complex joins, nested STRUCT/ARRAY types • Python coding — clean, efficient, well-tested • Distributed systems design • Beam pipelines (Google Cloud Dataflow) Common question: "You have a BigQuery table with nested repeated STRUCT. Write a query to unnest and compute per-user revenue."

Netflix Data Engineering Interview

Netflix focuses on Spark, Flink, and data platform infrastructure. • PySpark optimisation — partitioning, caching, skew handling • Stream processing with Flink or Spark Structured Streaming • Data quality and observability design • "What metrics would you monitor for a critical data pipeline?" Common question: "Design a pipeline to compute content engagement metrics (hours watched, completion rate) available within 5 minutes of a viewing event."

Databricks Data Engineering Interview

Databricks goes deepest on Spark internals and Delta Lake since they build the product. • Catalyst optimizer, AQE, Photon engine • Delta Lake MERGE, time travel, Z-ordering, OPTIMIZE • Streaming with Structured Streaming and Auto Loader • MLflow integration Common question: "Explain how AQE handles data skew differently from pre-Spark 3.0 approaches."

General Preparation Strategy

1. Practice writing PySpark and SQL code — reading is not enough, you need to run code. 2. Prepare 3-4 STAR stories for behavioural rounds (Situation, Task, Action, Result). 3. Study system design: know the medallion architecture, real-time vs batch trade-offs, Kafka basics. 4. Know your resume deeply — every line will be questioned. 5. Ask clarifying questions before designing — interviewers evaluate your problem-solving process, not just the answer.