Frame Databricks SQL User Problems

Company Context

Databricks is a leading data and AI platform used by enterprise data engineers, analysts, data scientists, and platform admins. One of its key surfaces, Databricks SQL, serves both technical and semi-technical users who need fast, reliable analytics workflows across queries, dashboards, and collaboration.

Problem

The UX team has been asked to improve the Databricks SQL query and dashboarding experience because adoption is growing, but user feedback is inconsistent. Product analytics show that weekly active users in Databricks SQL have grown 22% year over year, yet only 38% of new workspace users who open the SQL Editor create a second query within 14 days. Support tickets and research notes suggest multiple possible issues: query setup confusion, poor discoverability of saved queries, dashboard trust concerns, and friction switching between SQL Editor, queries, and dashboards.

Leadership wants a clear definition of the core user problem before committing design and engineering resources. You are the PM partner to a UX/UI designer and need to explain how you would use data and research to frame the problem correctly rather than jumping to solutions.

Deliverables

Describe your process for defining the core user problem for Databricks SQL using both quantitative data and qualitative research.
Identify which user segments you would prioritize first and why.
Explain how you would distinguish between symptoms, root causes, and edge-case complaints.
Propose how you would translate findings into a focused product opportunity statement and initial prioritization.
Define the success criteria that would tell you the problem framing is correct.

Constraints

You have 6 weeks to produce a recommendation for leadership.
Research bandwidth is limited to 12 user interviews and access to existing support tickets, product telemetry, and customer-facing teams.
Engineering cannot instrument a large new analytics pipeline during this discovery phase.
Any recommendation should be specific enough to guide a near-term UX redesign, not a multi-year platform rewrite.

Problem

Company Context

Problem

Deliverables

Describe your process for defining the core user problem for Databricks SQL using both quantitative data and qualitative research.
Identify which user segments you would prioritize first and why.
Explain how you would distinguish between symptoms, root causes, and edge-case complaints.
Propose how you would translate findings into a focused product opportunity statement and initial prioritization.
Define the success criteria that would tell you the problem framing is correct.

Constraints

You have 6 weeks to produce a recommendation for leadership.
Research bandwidth is limited to 12 user interviews and access to existing support tickets, product telemetry, and customer-facing teams.
Engineering cannot instrument a large new analytics pipeline during this discovery phase.
Any recommendation should be specific enough to guide a near-term UX redesign, not a multi-year platform rewrite.

Problem

Company Context

Problem

Deliverables

Describe your process for defining the core user problem for Databricks SQL using both quantitative data and qualitative research.
Identify which user segments you would prioritize first and why.
Explain how you would distinguish between symptoms, root causes, and edge-case complaints.
Propose how you would translate findings into a focused product opportunity statement and initial prioritization.
Define the success criteria that would tell you the problem framing is correct.

Constraints

You have 6 weeks to produce a recommendation for leadership.
Research bandwidth is limited to 12 user interviews and access to existing support tickets, product telemetry, and customer-facing teams.
Engineering cannot instrument a large new analytics pipeline during this discovery phase.
Any recommendation should be specific enough to guide a near-term UX redesign, not a multi-year platform rewrite.

Problem

Company Context

Problem

Deliverables

Describe your process for defining the core user problem for Databricks SQL using both quantitative data and qualitative research.
Identify which user segments you would prioritize first and why.
Explain how you would distinguish between symptoms, root causes, and edge-case complaints.
Propose how you would translate findings into a focused product opportunity statement and initial prioritization.
Define the success criteria that would tell you the problem framing is correct.

Constraints

You have 6 weeks to produce a recommendation for leadership.
Research bandwidth is limited to 12 user interviews and access to existing support tickets, product telemetry, and customer-facing teams.
Engineering cannot instrument a large new analytics pipeline during this discovery phase.
Any recommendation should be specific enough to guide a near-term UX redesign, not a multi-year platform rewrite.

Interview Guides

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Company Context

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Deliverables

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Company Context

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Frame Databricks SQL User Problems

Problem

Company Context

Problem

Deliverables

Constraints

Problem

Company Context

Problem

Deliverables

Constraints