Three Goldman Sachs Data Analyst candidates describe SQL, data cleaning, dashboards and behavioral questions, with one also reporting an online assessment, probability questions and a hashmap coding task.
$98K
Avg. Base Comp
$110K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Goldman Sachs Data Analyst reports lean on SQL, the practical analytics workflow and clear communication, though the three accounts here don't follow one fixed script. All three candidates ended without an offer, so these reports show what the questions looked like rather than a formula for winning the job.
One candidate applied online, took an HR call about their background and SQL experience, then faced a technical round on writing queries, cleaning data, building dashboard reports and working through a large dataset to present insights to stakeholders. Behavioral questions in that process covered teamwork, managing deadlines and communicating with stakeholders. Another had two screening rounds that mixed basic SQL with motivation questions, a walkthrough of a data project and a request to explain join types. The lesson there was to know when each join fits, not just how it is defined.
A third candidate described a heavier loop: an online assessment with DSA problems and math and time-complexity multiple choice, a HireVue, a roughly 45-minute phone interview on probability, regression and machine learning basics, and later a Python hashmap implementation. They found the process uneven from round to round.
Prepare one project story that runs from raw data to the decision it informed, and be ready to explain your query logic and findings to a nontechnical audience. If your loop includes an assessment, refresh basic data structures and probability as well.
Synthesized from 14 candidate reports by our editorial team.
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Real interview reports from people who went through the Goldman Sachs process.
The interview felt more like a conversation than a screening. The interviewer opened with a helpful overview of the team, the culture, and how the analytics function supports Marcus operations, which gave me useful context for the rest of the discussion. The technical questions centered on the reporting and analytics stack: Tableau, SQL, how I would automate recurring reporting, and how I would define and document metrics so others could rely on them. I was also asked how I would handle a discrepancy in published metrics, which tests whether you verify your own numbers before assuming the source is wrong.
The behavioral portion covered teamwork, how I would integrate into cross-functional teams, working with stakeholders, and culture fit. The conversation was professional and well-structured throughout, and I came away understanding that the role leaned toward operational analytics, customer experience reporting, and building scalable reporting systems rather than one-off analysis.
I did not receive an offer. A warm, conversational interview is not a reliable signal of the outcome, so I would prepare as thoroughly for the technical and metric-definition questions as for the behavioral ones.
Prep tip from this candidate
Be ready to walk through how you would reconcile a discrepancy in a published metric and then document the agreed definition, since both came up alongside the Tableau and SQL questions.
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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.
| Question | |
|---|---|
| Bagging vs Boosting | |
| Size of Joins | |
| Google Maps Improvement | |
| Append Frequency | |
| Comparing Search Engines | |
| Minimum Absolute Distance | |
| Production Model Monitoring | |
| Target Indices | |
| Success Measurement | |
| The Longest Journey | |
| SageMaker Deployment Architecture | |
| Deciding Between Solutions | |
| Shortest Path Algorithms | |
| Company Acquisition Choice | |
| Alternative Vendor Tradeoff | |
| Client Solution Pushback | |
| Credit Score Estimation | |
| Using APIs for Downstream Tasks | |
| Direct Mail | |
| Analyzing Multiple Data Sources | |
| Risk Model for a Mortgage Bank | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Monthly Customer Report |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates describe an opening conversation about their background. One applied online and had an HR call on SQL and analytics experience, meant to confirm that past work matched the role. The other's first screening mixed basic SQL with questions on why they wanted the job. Have a short account of your data work ready.
One Data Analyst candidate describes an online assessment with two easy and one medium DSA problem, multiple choice on basic math and time complexity, some system design concepts and a short HR essay. A mostly behavioral HireVue followed, with questions about large datasets and team conflict. The other reports do not mention these stages.
Technical questions covered writing SQL, explaining join types, cleaning data and producing dashboard reports. One candidate was asked how to analyze a large dataset and present the insights to stakeholders. Another had to walk through a past data project, the steps taken and the insights found.
One candidate had a phone interview of roughly 45 minutes with an analyst, covering probability and modeling, linear regression, machine learning basics and recursion-style brain teasers. Later in their process they were asked to implement a hashmap from scratch in Python. The other Data Analyst reports did not describe this depth.
Behavioral questions appear in all three reports. Candidates mention teamwork, managing deadlines, communicating with stakeholders, handling team conflict, motivation for the role and two situational questions. Prepare examples showing how you explained results to a nontechnical audience.
