Joins, window functions, aggregations over market/trade data
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Finance, Payments & Fintech
SQL + Python/ETL with a distinctive data-governance/compliance emphasis (GDPR/CCPA) on a BigQuery/Snowflake stack.
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And what its data engineers work with.
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Stages in the order candidates meet them.
5 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
1 reported question. See them below
distinctive
Medium confidence2 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
Data governance / complianceMedium confidence
Nobody reported these exact questions at Nasdaq. 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, window, aggregation)
Do first (3)Python ETL
Data governance/compliance (GDPR/CCPA, data quality); BigQuery/Snowflake
Market-data pipeline design
ThenProject deep-dive
Behavioral
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Why candidates were turned down, as reported.
Weak SQL
SQL is named in 37 of 50 company accounts.No data-governance/compliance awareness (important in finance)
Thin warehouse (BQ/Snowflake) experience
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3 reported, 2 composite. Each opens and closes. Composites are labelled.
In Bangalore in December 2025: a 90-minute online test, two technical rounds and an HR talk. Topics were Python, pandas, PySpark, Databricks and Linux commands, plus questions about the candidate's own projects.
What to take from it Know Linux commands and your own projects.
In Toronto in 2023: a screen, a take-home and three more rounds. Questions were about data processing and quality checks. The candidate had a bad experience.
What to take from it Be ready to explain how you check data quality.
About three rounds: an online test, an interview and a last technical round on databases. SQL was the clearest signal, with Python, PySpark and a LeetCode-style problem also appearing. One task asked for the top performers by yearly sales.
What to take from it SQL ranking questions are the safest thing to practise.
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 follows what is reported: a recruiter call, a technical round (SQL, Python, ETL, BigQuery or Snowflake), a manager round about data governance and privacy rules, then a senior panel with a project deep-dive and behavioral questions.
What to take from it Know the basics of data governance and privacy.
This is a composite, not one person's account. It is built from the loop we have on record for Nasdaq. Questions reported for this round: Joins, window functions, aggregations over market/trade data.
What to take from it Practise these questions before you reach this round.
Nasdaq DE first-hand reports are sparse; the governance/compliance angle is the distinctive prep area.
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Their reported questions, one at a time, against a stopwatch.
Joins, window functions, aggregations over market/trade 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 Nasdaq 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
Cloud and tools
Behavioral and communication
Read: Behavioral storytellingSay it aloud: Behavioral questions
Rehearse the Nasdaq loop
Run the question deckThen repeat your weakest topic once more.