Timed: SQL joins with aggregations + non-trivial algorithmic reasoning
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Big Tech & Enterprise Software
Screening then two technical rounds heavy on SQL joins/aggregations and PySpark basics + distributed computing.
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And what its data engineers work with.
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Stages in the order candidates meet them.
4 stages. 3 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
3 reported questions. 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.
Screening / OAMedium confidence
SQLHigh confidence
PySpark / SparkHigh confidence
Nobody reported these exact questions at IBM. 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 (joins + aggregations, window, nested) + optimization
Do first (2)PySpark basics (filter/join/top-N) + distributed computing
Python algorithmic
ThenClient ETL pipeline design
Behavioral/values
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Why candidates were turned down, as reported.
Weak SQL joins/aggregations under timed OA
SQL is named in 37 of 50 company accounts.Shaky PySpark/distributed fundamentals
Spark is named in 25 of 50 company accounts.Cannot adapt logic to changing requirements
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6 reported. Each opens and closes. Composites are labelled.
A technical screening, then SQL (joins, aggregations, windows, nested data) and PySpark (filter, join, top N, distributed computing), then behavioral questions.
What to take from it PySpark top-N and joins are likely.
Four rounds in Hyderabad: three technical rounds on the main stack (Azure and Spark) and a manager round with scenario questions.
What to take from it Know your main stack deeply.
In Bengaluru in 2021: basic Hadoop and PySpark questions, with checks on Python and SQL.
What to take from it Basics, but all of them.
In Pune: basic Spark architecture and lazy evaluation. Another candidate was asked about Z-ordering in PySpark and handling data skew.
What to take from it Be able to explain lazy evaluation and skew.
In Cairo: an interview on SQL, data modeling, Spark, Linux, Python and pipeline design.
What to take from it Prepare a broad set of topics.
In Cochin: a one-hour session with SQL scenario questions and pandas file-handling tasks.
What to take from it Practise reading and cleaning files in pandas.
Good first-hand signal; project/consulting-flavored. SQL + PySpark basics are decisive.
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Their reported questions, one at a time, against a stopwatch.
Timed: SQL joins with aggregations + non-trivial algorithmic reasoning
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what IBM 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
Python and coding
Read: Python chaptersDrill: Python practiceSay it aloud: Python questionsFull plan: Ace the Python round
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the IBM loop
Run the question deckThen repeat your weakest topic once more.