Window functions, CTEs, subqueries, GROUP BY pitfalls, indexing, optimization
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Big Tech & Enterprise Software
Whiteboard coding + SQL/processing + conceptual rounds; SQL optimization, Spark (RDD vs DF, Catalyst), and pipeline design.
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And what its data engineers work with.
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Stages in the order candidates meet them.
6 stages. 3 rounds have reported questions, described below.
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The topics it covers and the questions candidates reported.
2 reported questions. See them below
1 reported question. See them below
3 reported questions. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLMedium confidence
PythonMedium confidence
SparkMedium confidence
Nobody reported these exact questions at SAP. 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.
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Ranked by how much of the evidence points at each topic.
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Do the first group before the second.
SQL (window, CTE, optimization, GROUP BY pitfalls)
Do first (3)Spark (RDD vs DF, Catalyst, skew/shuffle)
Pipeline/lakehouse/CDC design (Lambda vs Kappa, idempotency)
Data modeling/warehousing (HANA)
ThenBehavioral
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Why candidates were turned down, as reported.
Weak SQL optimization/window
SQL is named in 37 of 50 company accounts.Shaky Spark internals
Spark is named in 25 of 50 company accounts.Shallow pipeline design (no idempotency/fault tolerance)
Design is named in 27 of 50 company accounts.08
3 reported, 2 composite. Each opens and closes. Composites are labelled.
A 45-minute video interview with SQL on a whiteboard, a design test and questions about object-oriented code. Other intern reports mention SQL and cleaning data.
What to take from it Practise writing SQL by hand.
In Berlin, the questions were about the candidate's ontology experience and why they wanted to join SAP.
What to take from it Link your past work to the team's topic.
Reports disagree on the pace. One candidate had a short process with little contact. Another felt it dragged on with no news.
What to take from it Ask the recruiter for dates at each step.
Composite walkthroughs. Built from the company's documented loop, not from one person's account.
This is a composite, not one person's account. It mixes SAP data science reports with general data engineering signals: a talk about your background, live Python, SQL (most active users per day), Spark (RDD against DataFrame, Catalyst), pipeline design and behavioral.
What to take from it Use it as a map. SAP-specific data engineering reports are rare.
This is a composite, not one person's account. It is built from the loop we have on record for SAP. Questions reported for this round: Window functions, CTEs, subqueries, GROUP BY pitfalls, indexing, optimization; Find the top 3 most active users per day.
What to take from it Practise these questions before you reach this round.
SAP DE-specific first-hand was sparse; verify the exact loop with your recruiter.
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Their reported questions, one at a time, against a stopwatch.
Window functions, CTEs, subqueries, GROUP BY pitfalls, indexing, optimization
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what SAP asks, strongest first. For the wider basics at your level, follow the Mid level plan alongside it.
Spark and big data
Read: Spark chaptersDrill: Spark playgroundSay it aloud: Spark questionsFull plan: Ace the Spark round
Pipeline and system design
Read: Data pipeline chaptersDrill: Pipeline practiceSay it aloud: Data pipeline questionsFull plan: Ace the pipeline round
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the SAP loop
Run the question deckThen repeat your weakest topic once more.