Openingloading the content…

Loadingone moment…

Big Tech & Enterprise Software

IBM

Enterprise IT / consulting / cloud · Data Engineering interview

Screening then two technical rounds heavy on SQL joins/aggregations and PySpark basics + distributed computing.

Cloud / platform

IBM Cloud + AWS/Azure/GCP per client; Spark, Hive, DataStage, Db2

Reported difficulty

Not reported

PySparkSQLHivePython

Hiring process

Technical screening

Technical (×2, senior ×3)

Behavioral / values

Offer

Round-by-round breakdown

Screening / OA

Medium confidence

Timed: SQL joins with aggregations + non-trivial algorithmic reasoning

SQL

High confidence

Multi-table joins, window functions, nested queries
Explain logic and adapt as requirements change
SQL optimization under pressure

PySpark / Spark

High confidence

Filtering, joining, top-N rows
Distributed-computing understanding
PySpark job writing

System & pipeline design prompts

Client-style ETL pipeline (ingestion → Spark processing → warehouse)

Prep — high priority

SQL (joins + aggregations, window, nested) + optimization
PySpark basics (filter/join/top-N) + distributed computing

Prep — medium priority

Python algorithmic
Client ETL pipeline design
Behavioral/values

Rejection patterns

Weak SQL joins/aggregations under timed OA
Shaky PySpark/distributed fundamentals
Cannot adapt logic to changing requirements

Reported experiences

Data Engineer (~2 YoE)

ReportedFirst-hand

Technical screening → SQL (joins/aggregations, window, nested) + PySpark (filtering/joining/top-N, distributed computing) → behavioral.

Data Engineer (3–5 YoE)

Q&A seriesFirst-hand

Medium PySpark interview-question series (worked solutions) reflecting IBM's PySpark emphasis.

Rehearse for IBM

Answered questions on what IBM 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

IBM

Enterprise IT / consulting / cloud · Data Engineering interview

Screening then two technical rounds heavy on SQL joins/aggregations and PySpark basics + distributed computing.

Cloud / platform

IBM Cloud + AWS/Azure/GCP per client; Spark, Hive, DataStage, Db2

Reported difficulty

Not reported

PySparkSQLHivePython

Hiring process

Technical screening

Technical (×2, senior ×3)

Behavioral / values

Offer

Round-by-round breakdown

Screening / OA

Medium confidence

Timed: SQL joins with aggregations + non-trivial algorithmic reasoning

SQL

High confidence

Multi-table joins, window functions, nested queries
Explain logic and adapt as requirements change
SQL optimization under pressure

PySpark / Spark

High confidence

Filtering, joining, top-N rows
Distributed-computing understanding
PySpark job writing

System & pipeline design prompts

Client-style ETL pipeline (ingestion → Spark processing → warehouse)

Prep — high priority

SQL (joins + aggregations, window, nested) + optimization
PySpark basics (filter/join/top-N) + distributed computing

Prep — medium priority

Python algorithmic
Client ETL pipeline design
Behavioral/values

Rejection patterns

Weak SQL joins/aggregations under timed OA
Shaky PySpark/distributed fundamentals
Cannot adapt logic to changing requirements

Reported experiences

Data Engineer (~2 YoE)

ReportedFirst-hand

Technical screening → SQL (joins/aggregations, window, nested) + PySpark (filtering/joining/top-N, distributed computing) → behavioral.

Data Engineer (3–5 YoE)

Q&A seriesFirst-hand

Medium PySpark interview-question series (worked solutions) reflecting IBM's PySpark emphasis.

Rehearse for IBM

Answered questions on what IBM 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