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Big Tech & Enterprise Software

Netflix

Streaming / entertainment · Data Engineering interview

Senior-heavy bar: heavy SQL, Python/Scala, petabyte-scale streaming design, and a real culture screen.

Cloud / platform

AWS-native; Spark, Flink, Kafka, Presto/Iceberg, feature stores

Reported difficulty

Not reported

SQLPythonScalaSparkFlinkKafka

Hiring process

Recruiter

Technical screen(s)

Onsite (4–5)

Culture round(s)

Offer

Round-by-round breakdown

SQL

Medium confidence

Top 10 most popular shows by number of viewers
Which genres are most popular in a region
How many new users signed up over a period

Python / Scala coding

Medium confidence

Implement simple data-processing logic
Clean, efficient code + CS fundamentals

Data Modeling / System Design

· L5+Medium confidence

Design scalable systems processing petabytes
Source & model data from application APIs
Data quality + unit testing

Culture

Medium confidence

Judgment, ownership, candor
High-impact stories

System & pipeline design prompts

Streaming events (Kafka), Spark/Flink real-time, feature stores, caching layers — billions of events
Petabyte-scale data platform

Prep — high priority

Heavy SQL (window functions, analytics)
Scalable design (Kafka/Spark/Flink/feature stores/caching, petabytes)
Clean Python/Scala; data quality + testing

Prep — medium priority

Data modeling from APIs
Netflix-culture STAR stories

Rejection patterns

Weak SQL at scale
Cannot design petabyte streaming
Thin data-quality/testing
Culture-fit mismatch (bar is high)

Reported experiences

Data Engineer

ReportedFirst-hand

Onsite: SQL-heavy (top-10 shows, genre-by-region, signups) + Python/Scala coding + data modeling/system design (Kafka/Spark/Flink/feature store/caching, petabytes) + culture.

Rehearse for Netflix

Answered questions on what Netflix actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences

Openingloading the content…

Big Tech & Enterprise Software

Netflix

Streaming / entertainment · Data Engineering interview

Senior-heavy bar: heavy SQL, Python/Scala, petabyte-scale streaming design, and a real culture screen.

Cloud / platform

AWS-native; Spark, Flink, Kafka, Presto/Iceberg, feature stores

Reported difficulty

Not reported

SQLPythonScalaSparkFlinkKafka

Hiring process

Recruiter

Technical screen(s)

Onsite (4–5)

Culture round(s)

Offer

Round-by-round breakdown

SQL

Medium confidence

Top 10 most popular shows by number of viewers
Which genres are most popular in a region
How many new users signed up over a period

Python / Scala coding

Medium confidence

Implement simple data-processing logic
Clean, efficient code + CS fundamentals

Data Modeling / System Design

· L5+Medium confidence

Design scalable systems processing petabytes
Source & model data from application APIs
Data quality + unit testing

Culture

Medium confidence

Judgment, ownership, candor
High-impact stories

System & pipeline design prompts

Streaming events (Kafka), Spark/Flink real-time, feature stores, caching layers — billions of events
Petabyte-scale data platform

Prep — high priority

Heavy SQL (window functions, analytics)
Scalable design (Kafka/Spark/Flink/feature stores/caching, petabytes)
Clean Python/Scala; data quality + testing

Prep — medium priority

Data modeling from APIs
Netflix-culture STAR stories

Rejection patterns

Weak SQL at scale
Cannot design petabyte streaming
Thin data-quality/testing
Culture-fit mismatch (bar is high)

Reported experiences

Data Engineer

ReportedFirst-hand

Onsite: SQL-heavy (top-10 shows, genre-by-region, signups) + Python/Scala coding + data modeling/system design (Kafka/Spark/Flink/feature store/caching, petabytes) + culture.

Rehearse for Netflix

Answered questions on what Netflix actually asks — each with the short answer, the answer that gets you rejected, and the follow-up.

Then build one and have it reviewed: design a daily orders pipeline or model a marketplace. No account.

All interview experiences