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Commerce, Travel & Marketplaces

Expedia

Online travel / marketplace · Data Engineering interview

Deep Spark internals (OOM, optimized joins, skew + salting), dimensional modeling, and batch+streaming (Spark+Kafka) design.

Cloud / platform

AWS; Spark, Hadoop, Hive, Kafka

Reported difficulty

Not reported

PySparkSQLPythonHadoopHive

Hiring process

Coding assessment (Python+SQL)

Hiring manager

Onsite (4) + code pairing

Offer

Round-by-round breakdown

SQL

Medium confidence

Intermediate–advanced (window functions, optimization) over bookings/clickstream

PySpark / Spark

· centralHigh confidence

OOM scenarios in Spark and how to fix them
Optimize a Spark application
Optimized joins in Spark
Data skewness & key salting

DSA

Medium confidence

Medium arrays (e.g., missing-number-style)

Data Modeling / LLD

Medium confidence

Dimensional modeling with product sense
OOP / class implementation (low-level design)

System & pipeline design prompts

Build batch + streaming pipelines (Spark + Kafka): explain choices, trade-offs, bottlenecks

Prep — high priority

Spark internals (OOM, optimize app, optimized joins, skew + salting); PySpark
SQL window functions
Batch+streaming design (Spark+Kafka)

Prep — medium priority

DSA (arrays); dimensional modeling (product sense)
LLD/OOP

Rejection patterns

Weak Spark internals (skew/OOM/joins)
Shallow dimensional modeling
No trade-off reasoning in design

Reported experiences

Data Engineer III (~5 YoE)

ReportedFirst-hand

Assessment (Python+SQL) → HM → onsite (4 rounds + code pairing). Heavy Spark: OOM fixes, optimize a Spark app, optimized joins, data skew + salting; DSA (missing-number-style); dimensional modeling; LLD (OOP/classes).

Data Engineer (Austin)

No offer (negative)First-hand

JoinTaro (Jun 2025) confirming the multi-round loop.

Rehearse for Expedia

Answered questions on what Expedia 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…

Commerce, Travel & Marketplaces

Expedia

Online travel / marketplace · Data Engineering interview

Deep Spark internals (OOM, optimized joins, skew + salting), dimensional modeling, and batch+streaming (Spark+Kafka) design.

Cloud / platform

AWS; Spark, Hadoop, Hive, Kafka

Reported difficulty

Not reported

PySparkSQLPythonHadoopHive

Hiring process

Coding assessment (Python+SQL)

Hiring manager

Onsite (4) + code pairing

Offer

Round-by-round breakdown

SQL

Medium confidence

Intermediate–advanced (window functions, optimization) over bookings/clickstream

PySpark / Spark

· centralHigh confidence

OOM scenarios in Spark and how to fix them
Optimize a Spark application
Optimized joins in Spark
Data skewness & key salting

DSA

Medium confidence

Medium arrays (e.g., missing-number-style)

Data Modeling / LLD

Medium confidence

Dimensional modeling with product sense
OOP / class implementation (low-level design)

System & pipeline design prompts

Build batch + streaming pipelines (Spark + Kafka): explain choices, trade-offs, bottlenecks

Prep — high priority

Spark internals (OOM, optimize app, optimized joins, skew + salting); PySpark
SQL window functions
Batch+streaming design (Spark+Kafka)

Prep — medium priority

DSA (arrays); dimensional modeling (product sense)
LLD/OOP

Rejection patterns

Weak Spark internals (skew/OOM/joins)
Shallow dimensional modeling
No trade-off reasoning in design

Reported experiences

Data Engineer III (~5 YoE)

ReportedFirst-hand

Data Engineer (Austin)

No offer (negative)First-hand

JoinTaro (Jun 2025) confirming the multi-round loop.

Rehearse for Expedia

Answered questions on what Expedia 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