Snap (Snapchat) Data Analyst Behavioral Interview Template

Purpose: Assess culture add and collaboration fit for a Data Analyst working on consumer social/AR products at Snap. This session emphasizes kindness in communication, crisp storytelling with data, fast-but-thoughtful execution, privacy-by-design thinking, and cross‑functional partnership with PM, Eng, Design, and Creative teams. What this interview covers at Snap: - Values alignment: Evidence of being kind, smart, and creative under pressure; ability to disagree respectfully and build trust across disciplines. - Product mindset for consumer social/AR: How you translate ambiguous product goals (e.g., Lenses, Camera, Stories, Spotlight, Ads surfaces) into measurable outcomes and experimentation plans. - Bias for action vs. analytical rigor: How you move quickly while safeguarding data quality, experiment validity, and user privacy. - Communication: Clear, concise, and visual storytelling tailored to non-technical partners; ability to influence without authority. - Privacy, safety, and integrity: Judgment around sensitive data, guardrails for experimentation, and trade‑offs between growth and user well‑being. Recommended 60‑minute flow: - 0–5 min: Warm‑up and context; outline competencies; quick timeline of the candidate’s recent roles. - 5–25 min: Deep dive Story 1 (high‑impact project on consumer product). Probe for your role, decision trade‑offs, stakeholder dynamics, metrics, and results. - 25–40 min: Deep dive Story 2 (conflict or challenge). Focus on kindness under disagreement, speed vs. rigor, data quality incidents, and learning. - 40–50 min: Snap product-sense mini‑case (behavioral lens). Example prompts: “Tell me about a time you defined success metrics for a new camera/lens/creator feature. How did you balance engagement with safety?” or “Describe how you handled an A/B test that yielded counterintuitive results and how you aligned PM/Design on next steps.” - 50–60 min: Candidate questions; close with expectations for next steps and collaboration style at Snap. Behavioral question bank (tailor 6–8 to role scope): - Tell me about a time you turned an ambiguous product ask into a measurable plan. How did you choose North Star and guardrail metrics? - Describe a disagreement with a PM/engineer/creative partner about an experiment or metric definition. How did you keep the conversation kind and productive? What changed because of your approach? - Walk me through an A/B test you owned that produced unexpected results. How did you validate, communicate, and decide whether to ship, iterate, or roll back? - Share an instance where speed was critical. What risks did you accept, what controls did you keep (data quality, privacy), and what was the outcome? - Tell me about a time you improved metric quality or logging for ephemeral content (e.g., Stories/Spotlight). What broke, how did you detect it, and what permanent fixes did you drive? - Give an example of influencing a roadmap using data storytelling (dashboards, memos, reviews). Who changed course and why? - Describe a situation where you prioritized user privacy or safety over a tempting growth levers. What trade‑offs did you make and how did you communicate them? - How have you supported creators/advertisers/partners with insights while protecting user experience? What good looks like (signals): - Specific, end‑to‑end ownership with clear personal impact; quantifies outcomes (e.g., +X% retention, –Y% crash rate, +Z% lens engagement) and acknowledges trade‑offs. - Uses a structured approach (STAR/CAR), keeps answers concise, and adapts depth based on interviewer cues. - Demonstrates product empathy for Snap’s camera/AR and social surfaces; thinks in terms of engagement, retention, creation, and safety metrics with guardrails. - Balances speed and rigor: pre‑registration, power, SRM checks, bucketing integrity, and data QA without analysis paralysis. - Communicates kindly and directly; escalates thoughtfully; builds durable cross‑functional relationships. Red flags: - Vague ownership or outcomes; over‑indexing on solo analysis without partner alignment. - Disregard for privacy/safety or for experiment quality (peeking, p‑hacking, no guardrails). - Adversarial communication style; inability to disagree without escalating conflict. - Gold‑plating or analysis paralysis that slows learning cycles. Evaluation rubric (score each 1–5, average for final): - Values & collaboration (kind, respectful, constructive influence) - Product sense for consumer social/AR (metrics, guardrails, trade‑offs) - Execution & experimentation craft (speed vs. rigor, data quality) - Communication & storytelling (clarity, audience‑aware, visuals/memos) - Privacy/safety judgment (defaults, risk identification, mitigation) Interviewer tips (Snap‑specific style): - Prefer crisp, concrete follow‑ups: “What did you personally do?” “What trade‑off did you make and why?” “How did your approach change partner decisions?” - Look for creativity in metrics and experimentation design fitting ephemeral content and AR use cases. - Invite brief visuals or frameworks if helpful, but timebox to keep the conversation fast and focused.