Upstart Data Analyst candidates report recruiter and hiring-manager conversations followed by SQL, metrics, risk-analysis, case, take-home, presentation, or onsite work depending on the process.
$140K
Avg. Base Comp
$165K
Avg. Total Comp
9 rounds
Typical Rounds
4-8 weeks
Process Length
Upstart Data Analyst interview reports point to a role that can combine practical analytics with lending and operational-risk context. Prepare to explain metrics you have owned, not merely name them. One candidate was asked how they defined and monitored Key Risk Indicators, including examples, data capture, monitoring infrastructure, and threshold methodology. That makes it worth practicing a concise explanation of how a metric is defined, monitored, and acted on when it changes.
SQL is the clearest technical theme. One assessment asked for cumulative default rate by Month-on-Book for a January 2026 loan cohort, with cohort size and a 90+ days-past-due default definition. Be ready to state assumptions around cohort membership, month calculations, and cumulative versus period default rates as you build the query. Other reports also mention SQL alongside product sense, coding, or a case study.
The later work may be broader than a timed query: candidates described take-homes, presentations, cross-functional conversations, onsite interviews, and in one case a leadership discussion. Prepare a short presentation-ready example that connects analysis to stakeholder decisions, and be able to discuss how you work across functions. Reports vary substantially in how far candidates progressed, so treat the sequence below as possible stages rather than a fixed format.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Upstart process.
I went through the HR and hiring manager rounds and seemed like a good fit for the role. The next step was a SQL assessment. Before the SQL assessment was due, HR reached out and said the role had been filled. The communication was prompt, which I appreciated.
Questions asked: The hiring manager asked about what metrics I owned. It was a 2LOD role, so I had to explain what a KRI was, what the monitoring infrastructure looked like, and how Key Risk Indicators for operational risk are defined. They asked for two examples, how I would capture them, and what threshold methodology I would use.
SQL assessment prompt: Write a SQL query to calculate the cumulative default rate by Month-on-Book (MOB) for loans originated in January 2026. Display the MOB, the total cohort size, and the cumulative default rate. MOB is the number of months since the loan was originated; a loan defaults at the MOB when it hits 90+ days past due.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter or HR screen focused on background, fit, and prior metrics work. Prepare a clear account of the analytics problems you have owned and ask clarifying questions if prompts are ambiguous.
Candidates report a hiring-manager conversation that may probe metric ownership. One Data Analyst candidate discussed KRIs, operational-risk monitoring infrastructure, example indicators, and threshold methodology.
Candidates report SQL, product-sense, coding, or case-based evaluation. One SQL prompt involved calculating cumulative loan default rates by Month-on-Book, so candidates may need to articulate cohort and default assumptions as well as write the query.
Some candidates report a take-home, case study, and/or presentation after earlier conversations. Prepare to communicate the decision relevance of your analysis and to teach an interviewer one useful aspect of your work.
Later stages reported by some candidates included cross-functional interviews, an onsite, additional hiring-manager discussion, and a VP of Analytics conversation. Candidates may be asked about cross-functional stakeholder management.
