One candidate's account of the BCG Data Engineer interview: a CodeSignal assessment, an online SQL and Python interview, then an in-person round on past projects, string problems and PySpark optimization.
$154K
Avg. Base Comp
$186K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The BCG Data Engineer interview described by one candidate moved from an online assessment to an online interview and then an in-person round. That candidate did not receive an offer.
The process opened with a CodeSignal assessment of four questions. The candidate found the interface clunky and solved only two, which was still enough to advance. Practice in a similar browser-based coding environment so tooling friction costs you less.
The online interview covered SQL and Python basics. One prompt asked the candidate to convert a SQL query into PySpark, which they found easy because it came down to mapping query logic onto DataFrame operations.
The in-person round was more detailed. Much of it covered work experience and the tech stacks used on past projects, so be ready to explain your own pipelines and tool choices. The Python questions focused on strings, including counting how often each character appears and reporting the first index of each. The candidate was also asked to optimize an existing PySpark snippet, which means spotting inefficiencies in code that already works. Looking back, they would have spent more time on PySpark optimization and on string problems that combine counting with positional tracking.
This is a single account, so treat the sequence and question types as one data point.
Synthesized from 1 candidate report by our editorial team.
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The in-person round is the one I keep thinking about, mostly because it was where the interview got specific. Before that, though, the process started with an online assessment on CodeSignal. The interface was clunky and hard to work in, and I only managed to solve two of the four questions, but that was enough to move me forward.
The next step was an online interview covering the basics. I was asked some SQL and Python questions, and one prompt asked me to convert a SQL query into PySpark. That one felt easy to me, since it mostly came down to mapping the query's logic onto DataFrame operations.
The in-person interview was where things got more detailed. A good chunk of it was about my work experience and the tech stacks I had used on past projects. The Python questions centered on strings, including one asking me to take a string, count how often each character appears, and report the first index where each character occurs. I was also asked to optimize a PySpark code snippet, which tested whether I could spot inefficiencies in code that already worked, not just write something from scratch.
I didn't get an offer. Looking back, I would have spent more time on the optimization side of PySpark and on string problems that combine counting with positional tracking, since those were the parts that pushed me hardest.
Prep tip from this candidate
Be ready to translate SQL into PySpark and to review a PySpark snippet for optimization, since both came up across the online and in-person rounds. Also practice string problems that track character counts alongside first-occurrence indices.
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Synthesized from candidate reports. Individual experiences may vary.
The candidate reports a CodeSignal assessment with four questions and found the interface clunky and hard to work in. They solved two of the four, which was enough to move forward. Practicing in a browser-based code editor may help, though one report cannot show the passing threshold.
The candidate describes an online interview covering SQL and Python fundamentals. One prompt asked them to convert a SQL query into PySpark, which they found easy because it mostly meant mapping the query's logic onto DataFrame operations. Be comfortable expressing joins, filters and aggregations both ways.
The candidate reports that much of the in-person round covered their work experience and past tech stacks. Python questions centered on strings, such as counting each character and returning its first index. They were also asked to optimize a PySpark snippet. They did not receive an offer and would have prepared more on these areas.
