Analytics-focused queries (joins, window, aggregation over financial data)
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Finance, Payments & Fintech
SQL analytics + strong Python/automation, Scala + Spark code, and a take-home on data extraction/transformation.
01
And what its data engineers work with.
02
Stages in the order candidates meet them.
5 stages. 4 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
1 reported question. See them below
1 reported question. See them below
1 reported question. See them below
1 reported question. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLMedium confidence
PythonMedium confidence
Scala / SparkMedium confidence
Take-homeMedium confidence
Nobody reported these exact questions at S&P Global. They are the questions a loop for each of its top topics is known to ask, so prepare for them as likely, not as confirmed.
05
Ranked by how much of the evidence points at each topic.
06
Do the first group before the second.
Python (ETL, automation)
Do first (3)SQL analytics
Scala + Spark basics; treat take-home as production (automation, tests, docs)
Databricks/Docker/K8s
ThenFinancial-data pipeline design
Behavioral
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Why candidates were turned down, as reported.
Weak Python/automation
Python is named in 13 of 50 company accounts.Shaky SQL analytics
SQL is named in 37 of 50 company accounts.Cannot write Scala/Spark
Spark is named in 25 of 50 company accounts.Poor take-home (no automation/tests)
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0 reported, 5 composite. Each opens and closes. Composites are labelled.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
This is a composite, not one person's account. Reports say the loop mixes SQL analytics, Python ETL, Scala or Spark and a take-home on automation. We could not read the original thread, so this is the shape of the loop only.
What to take from it Prepare SQL, Python and one automation project.
This is a composite, not one person's account. It is built from the loop we have on record for S&P Global. Questions reported for this round: Analytics-focused queries (joins, window, aggregation over financial data).
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for S&P Global. Questions reported for this round: Python programming, ETL, data manipulation; automation problems.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for S&P Global. Questions reported for this round: Scala code (easy–medium) + Spark code.
What to take from it Practise these questions before you reach this round.
This is a composite, not one person's account. It is built from the loop we have on record for S&P Global. Questions reported for this round: Data extraction, transformation, automation of data processes.
What to take from it Practise these questions before you reach this round.
Python + automation and the take-home (treat as production) are the gates.
09
Their reported questions, one at a time, against a stopwatch.
Analytics-focused queries (joins, window, aggregation over financial data)
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what S&P Global asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.
Python and coding
Read: Python chaptersDrill: Python practiceSay it aloud: Python questionsFull plan: Ace the Python round
Spark and big data
Read: Spark chaptersDrill: Spark playgroundSay it aloud: Spark questionsFull plan: Ace the Spark round
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Cloud and tools
Rehearse the S&P Global loop
Run the question deckThen repeat your weakest topic once more.