LinkedIn — Data Engineering Interview Experience

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Big Tech & Enterprise Software

Professional network / SaaS (Microsoft) · Data Engineering interview

Phone split 30/30 Python+SQL, then a loop emphasizing layered SQL, data modeling/ETL, and production-ready system design.

Cloud / platform

Azure + large on-prem; Kafka, Spark, Pinot, Hadoop

Reported difficulty

Not reported

SQLPythonSparkKafkaSnowflake

Hiring process

Recruiter

Phone (30 Python + 30 SQL)

Onsite (4–6)

Offer

Round-by-round breakdown

Phone screen

High confidence

30 min Python (data manipulation + algorithmic)
30 min SQL (joins, aggregations, window functions)

SQL

High confidence

Complex, layered queries — joins, aggregations, window functions
Sessionize member activity; top-N; dedup

Data Modeling / Architecture

High confidence

ETL, data warehousing, scalable architectures
Star/snowflake, SCD, partitioning

Statistics

Medium confidence

Basic statistics round

System & pipeline design prompts

Streaming vs batch; data lakes vs warehouses
Data quality, schema evolution, monitoring, alerting
Fault tolerance, consistency, maintainability over flashy architecture

Prep — high priority

Complex layered SQL + window functions
Data modeling/ETL/warehousing
Production-ready system design (streaming vs batch, lakes vs warehouses, monitoring)

Prep — medium priority

Python data manipulation + algorithms
Basic statistics
Kafka/Spark

Rejection patterns

Flashy architecture without fault tolerance/consistency/maintainability
Weak data modeling
Shaky layered SQL

Reported experiences

Data Engineer

ReportedFirst-hand

Phone (Python + SQL) → onsite (HM, SQL data manipulation, data architecture/modeling ETL, statistics, algorithms, system design).

Rehearse for LinkedIn

Answered questions on what LinkedIn 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

Professional network / SaaS (Microsoft) · Data Engineering interview

Phone split 30/30 Python+SQL, then a loop emphasizing layered SQL, data modeling/ETL, and production-ready system design.

Cloud / platform

Azure + large on-prem; Kafka, Spark, Pinot, Hadoop

Reported difficulty

Not reported

SQLPythonSparkKafkaSnowflake

Hiring process

Recruiter

Phone (30 Python + 30 SQL)

Onsite (4–6)

Offer

Round-by-round breakdown

Phone screen

High confidence

30 min Python (data manipulation + algorithmic)
30 min SQL (joins, aggregations, window functions)

SQL

High confidence

Complex, layered queries — joins, aggregations, window functions
Sessionize member activity; top-N; dedup

Data Modeling / Architecture

High confidence

ETL, data warehousing, scalable architectures
Star/snowflake, SCD, partitioning

Statistics

Medium confidence

Basic statistics round

System & pipeline design prompts

Streaming vs batch; data lakes vs warehouses
Data quality, schema evolution, monitoring, alerting
Fault tolerance, consistency, maintainability over flashy architecture

Prep — high priority

Complex layered SQL + window functions
Data modeling/ETL/warehousing
Production-ready system design (streaming vs batch, lakes vs warehouses, monitoring)

Prep — medium priority

Python data manipulation + algorithms
Basic statistics
Kafka/Spark

Rejection patterns

Flashy architecture without fault tolerance/consistency/maintainability
Weak data modeling
Shaky layered SQL

Reported experiences

Data Engineer

ReportedFirst-hand

Phone (Python + SQL) → onsite (HM, SQL data manipulation, data architecture/modeling ETL, statistics, algorithms, system design).

Rehearse for LinkedIn

Answered questions on what LinkedIn 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