Find the second highest salary from an employee table
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Finance, Payments & Fintech
HackerRank/live coding then a "Super Day": SQL window functions, Spark repartition-vs-coalesce, and Python data munging.
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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.
3 reported questions. See them below
2 reported questions. See them below
2 reported questions. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLHigh confidence
PythonMedium confidence
SparkHigh confidence
Nobody reported these exact questions at Mastercard. 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 (2nd-highest, ROW_NUMBER/RANK, window)
Do first (3)Spark (repartition vs coalesce, joins, avoid shuffle)
Python (flatten, exceptions)
Data modeling/warehousing (Redshift)
ThenPayments pipeline design
Behavioral
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Why candidates were turned down, as reported.
Weak window functions
SQL is named in 37 of 50 company accounts.Cannot explain repartition vs coalesce / shuffle avoidance
Spark is named in 25 of 50 company accounts.Sloppy Python error handling
Python is named in 13 of 50 company accounts.08
5 reported. Each opens and closes. Composites are labelled.
A recruiter call, an online assessment, then a Super Day with deep technical questions, design and behavioral.
What to take from it Plan for one long day with several rounds.
In Warangal: two rounds plus HR. The first covered the resume and projects, puzzles, guesstimates and advanced SQL. The second covered basic maths, puzzles and guesstimates.
What to take from it Practise puzzles and estimation, not only SQL.
Technical questions reported: how you tuned a Spark pipeline, how you used Python in data engineering, and a time you made sure data was good.
What to take from it Prepare one real example for each of those three.
Data queries first, then data modeling and behavioral questions, including a scenario-based modeling task and SQL.
What to take from it Practise modeling a small business scenario.
Behavioral questions asked how you explain a technical problem to a non-technical person, and why Mastercard. One report in Texas describes a 90-minute interview mixing technical and behavioral parts.
What to take from it Prepare a clear 'why Mastercard' answer.
Medium difficulty, practical (SQL/Spark/Python), not obscure DP puzzles.
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Their reported questions, one at a time, against a stopwatch.
Find the second highest salary from an employee table
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what Mastercard 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
Cloud and tools
Rehearse the Mastercard loop
Run the question deckThen repeat your weakest topic once more.