
Databricks Product Designer Case Interview: Lakehouse Governance & Collaboration Workflow
This Databricks case simulates designing a core Lakehouse experience for technical users. You will define and sketch an end-to-end workflow that helps data platform teams govern, share, and collaborate on trusted datasets across workspaces while preserving security and reproducibility. Scenario - A platform team needs to safely publish a curated Delta Lake table to internal analysts and an external partner. The solution should leverage Unity Catalog (metastore/catalog/schema/table), Delta Sharing, and integrate with notebooks, Jobs, and MLflow for lineage and reproducibility. Objectives - Frame the problem, identify primary personas (Platform Admin, Data Engineer, Data Scientist, Analyst, Security/Compliance), and articulate success metrics (e.g., time-to-first-query, policy violations avoided, adoption of shared assets, incident MTTR, user satisfaction for target personas). - Propose an information architecture that scales across multiple clouds/workspaces and clarifies account- vs workspace-level concepts. - Design the critical flows: discover a governed dataset; request/approve access with policy templates (RBAC, row/column-level permissions); share externally via Delta Sharing; preview/validate schema; view lineage and downstream impact; handle a breaking schema change incident and remediation. Constraints & Realities (call out trade-offs) - Multi-cloud (AWS/Azure/GCP) and enterprise scale; SCIM/SSO; token- and group-based access; PII handling and masking; schema evolution; ACID Delta tables; auditability; reliability during high concurrency; accessible design for dense data UIs. What you’ll do in the session - Clarify requirements and state assumptions. - Map the end-to-end journey and IA (navigation, empty/edge states, permissions states, error/alert patterns). - Sketch low-fidelity wireframes for 2–3 key moments (e.g., dataset details + preview, request/approval, lineage/impact view, Delta Sharing setup), narrating interaction details and rationale. - Define metrics, telemetry, and experiment ideas; propose a staged rollout and change management plan. - Discuss collaboration touchpoints with PM/Eng/UX Research and how you’d validate the design with customers. How it’s run (typical) - 5 min: brief intros and prompt. - 10 min: discovery and scoping. - 35–40 min: design exploration (whiteboard/FigJam/Figma low-fi). - 10–15 min: deep dive on a complex edge case (e.g., policy conflicts or schema-breaking change) and metrics. - 5 min: wrap-up and Q&A. What interviewers look for (Databricks-specific) - Technical product sense for data/AI workflows; ability to simplify complex, multi-tenant governance models without dumbing them down. - Clear systems thinking and IA that scale to large catalogs and varied personas. - Bias for practicality and iteration; crisp written/visual communication; ownership mindset; collaborative problem-solving with PM and Eng. - Evidence of measuring outcomes, not just outputs (meaningful metrics, instrumentation, and experiment plans).
75 minutes
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About This Interview
Interview Type
PRODUCT SENSE
Difficulty Level
4/5
Interview Tips
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