S&P Global

SQL analytics + strong Python/automation, Scala + Spark code, and a take-home on data extraction/transformation.

Cloud / platform

Azure (AzureDevOps) + AWS; Databricks, Docker, K8s, FastAPI

Reported difficulty

Not reported

PythonSQLSparkScalaDatabricks

Hiring process

Onsite loop (coding + design)

SQL analytics

Scala/Spark

Take-home

Offer

Round-by-round breakdown

4

01

SQL

Medium confidence

Analytics-focused queries (joins, window, aggregation over financial data)

02

Python

Medium confidence

Python programming, ETL, data manipulation; automation problems

03

Scala / Spark

Medium confidence

Scala code (easy–medium) + Spark code

04

Take-home

Medium confidence

Data extraction, transformation, automation of data processes

System & pipeline design prompts

1

Financial-data pipeline (automation, governance)

Rejection patterns

4

Weak Python/automation
Shaky SQL analytics
Cannot write Scala/Spark
Poor take-home (no automation/tests)

Reported experiences

1

Data Engineer

Blind thread (HELP)First-hand

A TeamBlind "Data Engineer interview at S&P Global" thread exists confirming the loop (body was login-walled). Composite otherwise: SQL analytics + Python ETL + Scala/Spark + take-home automation.

Hiring process

Round-by-round breakdown

SQL

Python

Scala / Spark

Take-home

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Engineer

Rehearse for S&P Global

S&P Global

Hiring process

Round-by-round breakdown

SQL

Python

Scala / Spark

Take-home

System & pipeline design prompts

Prep — high priority

Prep — medium priority

Rejection patterns

Reported experiences

Data Engineer

Rehearse for S&P Global