This case simulates a real ZoomInfo design challenge inside an engineering-led product team. You will explore how to streamline a core SalesOS workflow used by SDRs/Account Executives and RevOps admins while balancing data quality, compliance, and engineering constraints at scale. Prompt the candidate with a focused brief: “You’re designing the ‘Prospect List → Enrich → Export to CRM’ experience for ZoomInfo’s SalesOS. Today, SDRs build ICP-based lists, enrich records, de-duplicate, apply compliance gates (e.g., opt-outs), and export to Salesforce/HubSpot. Pain points include trust in data (stale contacts, duplicate accounts), friction during dedupe/merge, and uncertainty about export impact (ownership/routing). Redesign this end-to-end flow to improve speed to value and confidence in the data, without overwhelming users.” What we assess (specific to ZoomInfo’s culture): - Problem framing and prioritization: Can you quickly clarify user roles (SDR vs AE vs RevOps admin), goals (pipeline, efficiency), and constraints (data latency, match confidence, rate limits, CRM schema)? - Metrics-minded thinking: Define success metrics such as list creation time, enrichment coverage %, match confidence thresholds, duplicate reduction, export success rate, bounce rate, and downstream impact (meetings/bookings). Show how you’d instrument events and design for measurable outcomes. - Data quality and compliance UX: Handle stale data, role changes, opt-outs, regional regulations (e.g., GDPR/CCPA) with clear gating, explanations, and recoverable actions (e.g., replace suggestions, exclusion rules). - Enterprise workflow depth: Consider deduplication logic (contact vs account), bulk operations, ownership/routing visibility, required fields mapping, and safe defaults for large lists (1k–50k records). Balance power-user needs with discoverability. - Collaboration with engineering: Call out technical trade-offs (deterministic vs probabilistic matching, async processing, pagination/virtualization for large sets, error states and retries) and propose a phased MVP aligned to engineering effort. - Communication and stakeholder management: Structure your thinking, narrate trade-offs, and explain why your solution fits ZoomInfo’s GTM-centric, fast-iteration culture. Suggested 60-minute flow: - 5 min: Candidate clarifies personas, goals, constraints, success metrics. - 15 min: Map the current journey and identify highest-impact friction points (trust, dedupe, compliance, routing visibility). - 20 min: Propose solution: key screens and flows (List Builder with ICP criteria; Enrichment Review with confidence badges; Smart Dedupe queue with merge previews; Compliance & Ownership gate; Export summary with impact preview). Show phased delivery (MVP → enhancements) and analytics. - 10 min: Deep dive on two areas (e.g., match confidence UX and dedupe) including failure/edge cases and empty states. - 10 min: Q&A and trade-off discussion. Artifacts we expect during the case: - A concise user flow diagram from list creation to export. - Low/medium-fidelity sketches of 3–5 critical states (e.g., Filter/criteria panel, Enrichment Review with confidence chips and replace suggestions, Dedupe/merge preview, Compliance/ownership gate, Export summary with predicted CRM impact). - A success metrics plan: leading indicators (coverage, confidence, time to first export), guardrails (bounce rate, duplicate creation), and how you’d A/B test. What strong answers include: - Clear problem slicing (prioritizing trust and speed to value). - Opinionated defaults: export only records above a confidence threshold; queue ambiguous matches to a review lane; highlight routing/ownership before export. - Bulk-first interaction patterns (batch actions, keyboard shortcuts) with safe-guarded destructive operations. - Thoughtful empty/error states, async processing, progress affordances, and post-export feedback loops. - A pragmatic MVP with explicit engineering considerations and an iteration plan tied to measurable outcomes. Common pitfalls: - Treating this like a consumer UI task; ignoring data quality, compliance, and CRM realities. - Over-indexing on visuals vs. measurable impact. - Skipping dedupe/merge and ownership visibility. - No plan for instrumentation or experimentation. Interviewer notes template: - Discovery quality (questions asked, assumptions made). - UX depth (flows, states, edge cases). - Data/metrics rigor (KPIs, instrumentation, test plan). - Technical trade-offs (scalability, async, constraints alignment). - Communication and cultural fit (clarity, pace, bias for action).