Find the second highest salary in each department
Out loud, in order: clarify, build it, check it, explain it.
Practise SQLLoadingone moment…
Data engineering interview · Finance, Payments & Fintech
Strong SQL + PySpark with a production-first mindset; real-time card-transaction pipeline design with duplicate handling.
01
And what its data engineers work with.
02
Stages in the order candidates meet them.
4 stages. 2 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
3 reported questions. See them below
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By round. Questions marked Expected are written from the company's standards.
SQLHigh confidence
PySparkHigh confidence
Nobody reported these exact questions at American Express. 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 functions + logic-puzzle queries
Do first (3)PySpark tuning (skew, repartition vs coalesce, broadcast)
Real-time transaction pipeline design with dedup/idempotency
Data modeling for transactions
ThenData quality/validation stories
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Why candidates were turned down, as reported.
Lacking a production-first mindset (no data-correctness, validation, failure-handling)
Weak window functions
SQL is named in 37 of 50 company accounts.Cannot explain Spark skew fix or repartition vs coalesce
Spark is named in 25 of 50 company accounts.08
5 reported, 1 composite. Each opens and closes. Composites are labelled.
In Dallas in November 2024: a recruiter call, a round on one data engineering project, then coding in Python algorithms, SQL and PySpark. The candidate found it difficult and got no offer.
What to take from it Prepare all three coding areas, not just SQL.
In Gurgaon in October 2025: two technical rounds. One covered Python and data science basics. The other went deep on past projects and asked why you made each choice.
What to take from it Know the reason behind every step in your projects.
In Phoenix in 2026: an in-person panel of four interviewers who asked questions and had the candidate explain on a whiteboard. Topics were data modeling, a full pipeline design, SQL, Python, APIs and Airflow.
What to take from it Practise explaining on a whiteboard in front of a panel.
In Phoenix: the onsite asked for SQL on a whiteboard and for an explanation of which load types were used and why. No Python questions came up.
What to take from it Know full load against incremental load.
Scenario prompts: how you handled a failed reprocessing, and which technology you would pick for a product and why.
What to take from it Have a real story about a failed reprocessing run.
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 joins the topics reported by candidates: a recruiter call, SQL (second-highest salary per department, windows), PySpark (skew and repartition against coalesce), a design round on a real-time card transaction pipeline with duplicate handling, and a behavioral round on owning a production incident.
What to take from it Use it as a map of the loop.
Production-first mindset (correctness, validation, failure handling) is explicitly valued.
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Their reported questions, one at a time, against a stopwatch.
Find the second highest salary in each department
Out loud, in order: clarify, build it, check it, explain it.
Practise SQL10
Pick your level and the weeks you have.
Built from what American Express 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 American Express loop
Run the question deckThen repeat your weakest topic once more.