Company Context

Databricks is a leading data and AI platform used by enterprises to build data pipelines, run analytics, and develop ML/AI applications on the Lakehouse Platform. A key growth lever is converting new workspace users from initial sign-up into active users of core surfaces such as Databricks Workspace, notebooks, SQL Editor, and Unity Catalog.

Problem

Databricks has seen strong top-of-funnel growth in new workspace invitations, but activation is inconsistent. Internal data shows that only 42% of invited users complete a meaningful first task within 7 days, and UX research suggests many users are unsure what to do first after landing in the workspace. Different personas—data analysts, data engineers, and ML practitioners—arrive with different goals, but the current first-run experience is largely generic.

You are a PM partnering with a UX/UI Designer to outline the step-by-step flow a user takes to achieve their goal in Databricks. Your task is not to redesign every screen, but to define the ideal end-to-end user flow for one high-value first-run job to be done, identify friction points, and recommend which moments deserve design investment first.

Deliverables

1. Identify the target user segment and the specific first-run goal you would optimize for in Databricks.
2. Map the user journey step by step from workspace entry to successful task completion, including decision points, failure points, and moments of confusion.
3. Prioritize the top UX improvements or product interventions that would reduce friction in that flow.
4. Define how you would measure whether the new flow improves activation and user confidence.

Constraints

• You can optimize only one primary first-run flow in the next quarter.
• Design and engineering capacity is limited to a small cross-functional squad for 10 weeks.
• You should avoid requiring major backend platform changes.
• Any solution must work across enterprise environments with varying permissions, data availability, and admin setup.