This behavioral interview probes how a Data Analyst in ZoomInfo’s Engineering org drives measurable business impact in a fast-paced, revenue‑oriented, and data‑quality‑obsessed culture. Expect deep dives into: 1) Metrics‑driven storytelling — framing problems, isolating signal from noise, and quantifying outcomes with GTM metrics (pipeline lift, lead‑to‑opportunity conversion, ARR impact, churn/retention, adoption). 2) Cross‑functional partnership — collaborating with RevOps, Marketing Ops, Sales leadership, Product, and Data Engineering; handling conflicting priorities; pushing back constructively; influencing without authority. 3) Data judgment and quality trade‑offs — coverage vs. accuracy vs. freshness; defining SLAs; incident response when a data issue affects customers or sales workflows; root‑cause narratives and prevention. 4) Ownership and urgency — bias to action, handling ambiguity, prioritizing high‑leverage work, closing the loop with stakeholders, and learning from misses. 5) Compliance and ethics mindset — thoughtful handling of PII and governance (e.g., GDPR/CCPA considerations), documenting decisions, and aligning analyses with ZoomInfo’s standards. 6) Communication — crisp, non‑technical explanations for go‑to‑market audiences and clear written/visual updates (dashboards, briefs). Typical flow: 5 min intro and context; 25–30 min STAR‑structured deep dives across 2–3 past projects (e.g., improving enrichment match rates, diagnosing intent‑signal drift, redesigning a KPI dashboard used by Sales); 10 min situational prompts (prioritization under deadline, stakeholder conflict, data incident); 5 min candidate Q&A. Interviewers look for concrete numbers, stakeholder mapping, and evidence of customer‑oriented thinking aligned to ZoomInfo’s focus on reliable B2B data and execution velocity.