AI for Data Engineers

Workflow-as-infrastructure for modern data engineering teams

Woven takes on the continuous data upkeep created by constant product and business change — so your team can focus on stakeholder impact instead of invisible maintenance.

Works with GitHub, dbt, Snowflake, and Slack. No workflow changes required.

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Upstream

Products ship. Schemas drift.

Every feature introduces schema changes that ripple downstream. Field meanings shift. New entities appear. Breaking changes hide in routine PRs — owned by teams who don't see the downstream impact.

Downstream

Business evolves. Usage shifts.

Stakeholders ask new questions through ad-hoc queries, not formal requests. Emerging patterns live in Snowflake logs, not roadmaps. Intent is implicit, scattered, and invisible to data teams.

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- Trigger workflows based on Application schema signals
  Handles continuous product-driven schema change by resolving semantic meaning at the source and preparing review-ready downstream data engineering work.
- Trigger workflows based on Analytics usage signals
  Watches how the business is actually using data and prepares review-ready analytics model evolutions that amplify decision-making value.

Let’s design the future of data engineering together.

Hire Woven to absorb continuous data change without adding headcount or operational load — so data stays aligned as the product and business evolves.

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AI for Data Engineers

Workflow-as-infrastructure for modern data engineering teams

Problem

Unplanned data toil is rarely ticketed and always growing — until it breaks.

Schemas change and usage evolves every week, but reconciling the fallout is manual and competes with stakeholder requests — so it’s easy to defer until urgency is forced or something breaks.

Products ship. Schemas drift.

Business evolves. Usage shifts.

Solution

Woven absorbs the ongoing data work your team doesn’t have capacity for.

Woven handles the follow-up created by product and business change — surfacing urgent issues immediately and batching the rest into review-ready work.

How it works

Signal → Plan → PR → Propagate

Woven continuously identifies, prioritizes, and prepares the data engineering work created by upstream schema change and downstream usage change. No tickets or manual triage.

See an example

Let’s design the future of data engineering together.