
Goldman Sachs Behavioral Interview Template — Data Analyst (Engineering)
This behavioral interview reflects common Goldman Sachs practices observed across Superday and experienced-hire rounds for Data Analysts embedded in engineering teams that partner with Global Markets, Investment Banking, Asset Management, and Consumer & Wealth Management. It evaluates how you operate in a high-stakes, client-first culture emphasizing integrity, excellence, partnership, and strong risk/control mindset. Structure (approx.): 1) Warm-up and role context (5 min) — Brief introductions, why Goldman Sachs and why this team; understanding of how a data analyst enables front-office, risk, and operations outcomes. 2) Core competencies deep dive (35–40 min) — Expect probing follow-ups and drill-downs on: • Client and stakeholder management: navigating traders/bankers/PMs, risk/compliance, and engineering; handling conflicting priorities; setting expectations across time zones. Sample prompts: “Tell me about a time you reconciled competing requests from front-office and risk,” “Describe a situation where you pushed back on a senior stakeholder to protect data quality.” • Ownership under pressure: operating during live market events, earnings, or deal deadlines; prioritization and trade-offs. Prompts: “Walk me through a high-pressure incident where your analysis changed the plan within hours,” “Describe a time you re-scoped work to hit an immovable deadline.” • Data quality, governance, and controls: lineage, validation, auditability, and documentation aligned to regulatory expectations (e.g., handling sensitive data/MNPI appropriately). Prompts: “Give an example of instituting checks that prevented a costly downstream error,” “When did you escalate a control or compliance concern?” • Analytical storytelling and communication: translating complex findings into executive-ready narratives for non-technical stakeholders; influencing decisions with evidence. Prompts: “Share a time your dashboard/analysis drove a revenue, cost, or risk reduction outcome,” “How did you tailor the message differently for a trader vs. a compliance officer?” • Collaboration and conflict resolution: partnering with platform engineering, data engineering, and product; resolving disagreement with humility and facts. Prompts: “Describe a disagreement about methodology/metric definition and how you resolved it.” • Learning agility and continuous improvement: post-mortems, iteration, mentoring, and upskilling. Prompts: “What did you change after a failed analysis or incident review?” 3) Behavioral deep-dive case (5–10 min) — One end-to-end scenario (e.g., building a control-hardened P&L attribution view or a liquidity-risk dashboard) focusing on problem framing, stakeholders, controls, and outcome measurement. 4) Candidate questions (5 min) — Expect thoughtful questions about data governance maturity, business KPIs, model/report approval flows, and success metrics for the first 90 days. Interview style: highly structured, rapid follow-ups, and STAR-driven. Interviewers frequently test for specifics (volumes, SLAs, defect rates, impact quantified in bps/revenue/cost/risk), evidence of ethical judgment with sensitive data, and readiness to escalate issues. Strong answers show measurable business impact, clear stakeholder mapping, concrete control measures, and reflection (what you’d do differently). Tailor examples to GS contexts: live deal support (IBD), market close reconciliations (Global Markets), investor reporting (Asset Management), or wealth client analytics (CWM).
8 minutes
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About This Interview
Interview Type
BEHAVIOURAL
Difficulty Level
4/5
Interview Tips
• Research the company thoroughly
• Practice common questions
• Prepare your STAR method responses
• Dress appropriately for the role