Usage evidence is visible while the business result behind it remains disputed.
Data engineers need a credible value story, while executives need proof that AI spending earned its place.
Who you’d be doing this for
“My VP asked what this pipeline changed for the business, and I had three dashboards open with no answer.”
Minseo Jang · Lead Data Engineer
Owns production data pipelines and must explain why their platform investment should remain funded.
What is at stake
Pilot customers have production workloads, but their renewal conversations lack agreed evidence of business value. You have to weigh a repeatable programme against the different outcomes each customer expects.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- repeatable customer programme vs bespoke value assessment
- platform evidence vs business attribution
- executive proof vs field capacity
- cost scrutiny vs expansion opportunity
Why Databricks
Databricks workloads span data engineering, analytics, and AI, so customer value must connect technical adoption to the business outcome that sustains consumption.
Written with these in mind
Not your kind of problem? 107 more at Databricks, or browse every organization.
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.