Goldman Sachs Data Analyst candidates report an initial background screen followed by SQL, analytics-workflow, stakeholder-communication, and behavioral discussion. One report describes two screening rounds.
$81K
Avg. Base Comp
$94K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
Goldman Sachs Data Analyst interviews in the available reports focus on whether you can explain practical analytical work clearly. SQL is the most consistently named technical area. One candidate described two screening rounds: the first combined basic SQL, behavioral questions, motivation, and a walkthrough of a data project; the second returned to SQL joins and included situational questions. Be ready to explain both what each join does and when you would choose it.
A separate candidate reported an HR screen followed by a technical conversation about writing SQL, cleaning data, dashboard reporting, and approaching a large dataset for business insights. That account also included questions about presenting findings to stakeholders, teamwork, deadline management, and stakeholder communication. Prepare one concrete project narrative that moves from the data and analysis steps to an insight, dashboard or report, and an action-oriented explanation for a business audience.
The reports describe different interview content, so treat them as preparation themes rather than one fixed script. Focus your examples on your own analytical decisions, communication, and the practical impact of the work.
Synthesized from 2 candidate reports by our editorial team.
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Real interview reports from people who went through the Goldman Sachs process.
What stood out most was how closely the interview stayed tied to practical analytics work rather than purely theoretical questions. I applied online and was then invited to an HR screening call. The recruiter walked through my background and asked about my experience with SQL and data analytics. It was a straightforward initial conversation designed to establish whether my previous work matched what the role required.
The next stage was a technical round covering several parts of a typical analyst workflow. I was asked about writing SQL queries, cleaning data, producing dashboard reports, and approaching problem-solving scenarios. One broader question asked how I would work through a large dataset to identify useful business insights and then present those findings to stakeholders. That question required me to think beyond the analysis itself and explain how I would communicate the results in a way that supported business decisions.
There were also behavioral questions about working with a team, managing deadlines, and communicating with stakeholders. Overall, the process felt organized and professional. I would describe the difficulty as moderate for someone who already has hands-on analytics experience, since the questions covered both technical execution and the practical communication expected from an analyst. I ultimately did not receive an offer. My main takeaway is to prepare examples that show your full process, from querying and cleaning data through building a dashboard and explaining the resulting insights to a nontechnical audience.
Prep tip from this candidate
Prepare a concrete walkthrough of how you would analyze a large dataset, clean it with SQL, turn the results into a dashboard, and present actionable insights to stakeholders. Also have specific examples ready about teamwork, deadline management, and stakeholder communication.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Goldman Sachs
Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial screening that may cover prior analytics experience, SQL familiarity, why they want the Data Analyst role, and how their background relates to it. Prepare a concise resume walkthrough and role-motivation answer.
Candidates report technical discussion of SQL queries and join types, plus questions about cleaning data, dashboards, and how they analyzed a project. Explain your choices and the insights produced, not just the tools you used.
Candidates report situational and behavioral questions alongside practical analytics topics. You may be asked how you manage deadlines, work with a team, communicate with stakeholders, or turn a large dataset into useful business insight.
