PERSONA
Data Engineer
Data Engineers build and maintain the pipelines that move and transform data for analytics, machine learning, and AI.
They work with complex workflows where small mistakes can quietly break everything downstream.
PAIN POINTS
Operator discovery is slow and technical
Misconfigured nodes fail only after long runs
Debugging is slow and scattered
DESIGN RESPONSE
Reduce risk at every step of the workflow.
Clarity before composition
Minimize incorrect operator usage
Safety during configuration
Block invalid configurations
Observability during execution
Make every run traceable
Workflow builder layout
The workflow builder is designed as a single, continuous workspace where Data Engineers can build, run, and review pipelines without switching context. Operators, inputs, and outputs remain accessible at all times, while the same canvas is used to construct workflows and inspect their execution.
Runs, schedules, and past results are attached directly to the workflow, allowing engineers to understand what was built, what ran, and what happened in one place. This layout creates a stable mental model of the system, which is essential before users can confidently choose and compose operators.
1.1 Default Builder canvas highlighting core components
1.2 Run history showing Current/Past Runs
Finding the right operator
Modern AI workflow builders often expose dozens of operators, each with technical names and overlapping capabilities.
Without enough context, engineers are forced into trial-and-error: add a node, configure it, run the pipeline, and only then discover whether it was the right choice.
Runs, schedules, and past results are attached directly to the workflow, allowing engineers to understand what was built, what ran, and what happened in one place. This layout creates a stable mental model of the system, which is essential before users can confidently choose and compose operators.
