The setting
' is built on Apache Spark. It gives teams one workspace for SQL analytics, batch and streaming processing, and building and deploying models for machine learning.
An unfinished agent session holds useful context without a shared understanding of who acts next.
Engineers want continuity, while analysts want a clear distinction between observing an investigation and continuing it.
Who you’d be doing this for
“Yesterday I got four screenshots and a message saying to carry on. I still had to call the engineer to ask what they'd ruled out.”
Hong Ma · Analytics Engineer · a character in this brief
Receives unfinished investigations from data engineers and translates them into answers for business colleagues.
What is at stake
Six of eight interviewed engineers rebuild unfinished investigations through screenshots. You have to weigh continuity inside a live session against an analyst's need for a clear, bounded starting point.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- live continuity vs a stable starting point
- shared context vs separate acting authority
- mobile reach vs a coherent first release
- sender convenience vs recipient comprehension
Why Databricks
Omnigent sessions bring data engineers and analysts into agent-driven work where shared context and execution authority remain different things.
Written with these in mind
Not your kind of problem? 115 more at Databricks, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Databricks. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Product Design New Grad (2027 Start) (opens in a new tab)
- Sr. Product Designer, AI/BI (opens in a new tab)
- Enable collaboration between data engineers, analysts, and business leaders
- Help define new design patterns for AI-native enterprise software
- Sr. Product Designer (opens in a new tab)
- Sharpen the problem, set the design direction, and carry the work from framing and interaction models through visual polish and shipped detail.
- Use interactive prototypes as a primary thinking tool to explore ambiguous problems, reduce risk, and align the team on a direction.
- Staff Product Designer, AI Products (opens in a new tab)
- Define a new generation of agent-driven workflows that help users move seamlessly from data and insights to decisions and action.
- You will help shape agent-driven experiences that make the power of the Lakehouse accessible to a broader set of users.
- Product Design Intern (2027 Start) (opens in a new tab)
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
The same setting, from another role
Each is set around Databricks’s Unified cloud-based analytics platform (Apache Spark-based), written for a different role. They’re separate briefs, not the same scenario.
The Agent Bricks campaign has competing evidence about whether the audience or message is limiting response.
You have to decide what AI recommends, what strategists review, and how the pilot proves it helped.
Data engineers are leaving technical pages before they begin an evaluation, and the pilot must generate 120 qualified evaluations.
You have to weigh a narrow first-workload story against the broader platform narrative and follow-up quality.
Enterprise data engineers are opening multiple lookalike assets before finding one they can use.
You must weigh faster discovery against permission-aware relevance and trust.