snowflake

Snowflake Product Designer Case Interview — Designing Governed Data Sharing in Snowsight

This case simulates a real Snowflake product design challenge focused on enterprise data workflows, governance, and cost-aware UX within Snowsight (Snowflake’s web UI). You will frame, design, and communicate an end-to-end experience that enables a data owner to safely share a governed dataset with internal teams and external partners, while allowing consumers to discover, request/access, and monitor usage — all without creating undue cost or security risk. What the case covers at Snowflake: - Problem framing: Clarify business goals (safe data sharing to accelerate analytics and app dev), success metrics (time-to-first-query, adoption of shares/listings, query success rate, cost/credit efficiency, reduction in access tickets), and constraints (must align with Snowsight patterns; work across accounts, clouds, and regions). - Core flows to design: 1) Producer/Admin: create a governed share or marketplace listing; choose objects (databases/schemas/tables/views); apply policies (RBAC roles, dynamic data masking, row access policies); set data refresh/replication; define cost guardrails (resource monitors, warehouse auto-suspend guidance, previews vs full access). 2) Consumer: discover a dataset (search/browse with clear metadata/lineage), request access or subscribe, preview with sample rows, understand data quality/freshness, run first query safely, and monitor usage. 3) Governance/FinOps: audit access (who queried what and when), monitor spend and performance, receive alerts on thresholds, and remediate misconfigurations. - Enterprise UX specifics Snowflake expects: permission clarity (who can see what and why), safe defaults, progressive disclosure for advanced settings, clear states for cross-account/cross-region behavior, and informative empty/error states (e.g., masked columns, missing privileges, suspended warehouses). - IA and modeling: propose an information architecture that fits Snowsight (left-nav objects, contextual panels, setup wizards) and a permissions mental model that surfaces effective access (inherited vs direct) without overwhelming users. - Data quality and trust: show how lineage, usage signals, and publisher reputation help consumers decide; design labels for PII and policy impacts (e.g., masked columns) and communicate freshness/replication status. - Cost awareness: integrate credit/cost cues where decisions are made (warehouse size, preview vs full run), default to efficient behaviors (auto-suspend, result caching hints), and provide simple what‑ifs before expensive actions. - Rollout and measurement: propose GA path (private preview → public preview → GA), define north-star and guardrail metrics, and outline an experiment to validate comprehension of permissions/costs. - Collaboration: demonstrate how you’d partner with PM/ENG/Legal/Security, handle technical constraints (e.g., existing RBAC, shares vs listings, reader accounts), and plan for accessibility and localization. Format and timing (live, collaborative): - 0–5 min: Brief, prompt, and clarifying questions. - 5–15 min: Problem framing, users/personas (Data Platform Admin, Data Owner, Analyst/Scientist, FinOps/Governance), success metrics, and constraints/assumptions. - 15–40 min: Sketch task flows and low‑fi wireframes for producer, consumer, and governance views; permission/cost surfaces; empty/error states. - 40–55 min: Deep dive on edge cases (masked PII, cross‑cloud replication lag, revoked access, warehouse suspended, failed subscription) and trade‑offs. - 55–70 min: Rollout plan, metrics/experiments, and Q&A. What interviewers evaluate (Snowflake style): - Customer obsession and clarity of scope under ambiguity. - Systems thinking for complex, multi‑persona, policy‑heavy workflows. - Practical enterprise UX craft: state management, IA, and comprehension of RBAC/masking/costs. - Data‑driven mindset: measurable outcomes and experiment design. - Communication and collaboration: crisp rationale, trade‑offs, and next steps. Artifacts expected during the session: problem statement, user/permission model, key flows, low‑fi wires, state/edge‑case inventory, metrics and rollout notes. Common follow‑ups: extend to external data sharing via Marketplace listings; design a "request access" workflow with approvers and SLAs; add a usage dashboard that correlates spend to value; or adapt the design for highly regulated data (HIPAA/PCI) with stricter policy defaults.

engineering

70 minutes

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About This Interview

Interview Type

PRODUCT SENSE

Difficulty Level

4/5

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