Designing Databricks UX With Data | Dataford Interview Questions

Company Context

Databricks is a leading data and AI platform used by enterprises to build data pipelines, run analytics, and develop machine learning and generative AI applications. Its products span technical surfaces like Databricks Workspace, Lakeflow, SQL Editor, AI/BI Dashboards, and Mosaic AI, serving both highly technical practitioners and less technical business users.

Problem

A design leader at Databricks wants to understand how you would shape product decisions in an environment where craft quality and measurable impact both matter. Internal feedback shows a recurring tension: some teams optimize heavily for usability metrics and experimentation, while others prioritize polished workflows and visual coherence but struggle to prove business impact. In recent usability studies, new users in Databricks Workspace reported that multi-step tasks such as creating a notebook, connecting to data, and sharing results feel powerful but cognitively heavy. At the same time, product leadership does not want design quality reduced to only click-throughs or task times.

Your challenge is to propose how you would approach designing a better end-to-end experience for one Databricks surface while balancing intuition, creativity, and analytical rigor.

Deliverables

Choose one specific Databricks surface or workflow to improve and explain why it is the right place to focus.
Identify the primary user segment, their core job to be done, and the biggest pain points in the current experience.
Describe your design process, including how you would combine qualitative design craft with quantitative product thinking.
Prioritize the most important experience improvements for an MVP and explain key trade-offs.
Define how you would measure whether the redesign improved both user experience and business outcomes.

Constraints

You have one quarter to deliver an MVP.
Design and research support are limited to 1 product designer, 1 UX researcher, and a shared product analytics partner.
Engineering can support only moderate front-end changes; major platform re-architecture is out of scope.
The solution must work for existing enterprise customers without disrupting critical workflows.

Problem

Company Context

Problem

Your challenge is to propose how you would approach designing a better end-to-end experience for one Databricks surface while balancing intuition, creativity, and analytical rigor.

Deliverables

Choose one specific Databricks surface or workflow to improve and explain why it is the right place to focus.
Identify the primary user segment, their core job to be done, and the biggest pain points in the current experience.
Describe your design process, including how you would combine qualitative design craft with quantitative product thinking.
Prioritize the most important experience improvements for an MVP and explain key trade-offs.
Define how you would measure whether the redesign improved both user experience and business outcomes.

Constraints

You have one quarter to deliver an MVP.
Design and research support are limited to 1 product designer, 1 UX researcher, and a shared product analytics partner.
Engineering can support only moderate front-end changes; major platform re-architecture is out of scope.
The solution must work for existing enterprise customers without disrupting critical workflows.

Problem

Company Context

Problem

Your challenge is to propose how you would approach designing a better end-to-end experience for one Databricks surface while balancing intuition, creativity, and analytical rigor.

Deliverables

Choose one specific Databricks surface or workflow to improve and explain why it is the right place to focus.
Identify the primary user segment, their core job to be done, and the biggest pain points in the current experience.
Describe your design process, including how you would combine qualitative design craft with quantitative product thinking.
Prioritize the most important experience improvements for an MVP and explain key trade-offs.
Define how you would measure whether the redesign improved both user experience and business outcomes.

Constraints

You have one quarter to deliver an MVP.
Design and research support are limited to 1 product designer, 1 UX researcher, and a shared product analytics partner.
Engineering can support only moderate front-end changes; major platform re-architecture is out of scope.
The solution must work for existing enterprise customers without disrupting critical workflows.

Problem

Company Context

Problem

Your challenge is to propose how you would approach designing a better end-to-end experience for one Databricks surface while balancing intuition, creativity, and analytical rigor.

Deliverables

Choose one specific Databricks surface or workflow to improve and explain why it is the right place to focus.
Identify the primary user segment, their core job to be done, and the biggest pain points in the current experience.
Describe your design process, including how you would combine qualitative design craft with quantitative product thinking.
Prioritize the most important experience improvements for an MVP and explain key trade-offs.
Define how you would measure whether the redesign improved both user experience and business outcomes.

Constraints

You have one quarter to deliver an MVP.
Design and research support are limited to 1 product designer, 1 UX researcher, and a shared product analytics partner.
Engineering can support only moderate front-end changes; major platform re-architecture is out of scope.
The solution must work for existing enterprise customers without disrupting critical workflows.