VP, Data Platform & Engineering - name

We are hiring a VP of Data Platform & Engineering to make WISEcode data the easiest and most trustworthy data to consume, inside and out, and to own the machine that ships it safely.

This is not merely a pipeline-plumbing role. You will own and develop a portfolio of data products, the engineering motion that ships them, and the trust boundary that protects the corpus from leaking into external AI systems. You set the AI-native engineering standard the rest of the data org builds to.

Your pillar spans:

Platform: a portfolio of internal data products, delivered as authenticated capabilities and contracts.

Engineering: the shipment motion that moves a food record from raw input to scored, published output, automated and agentic.

Data security and governance: the trust boundary, least-privilege access, and the enforcement of our corpus-stewardship doctrine.

About this Role

As VP of Data Platform & Engineering, you own the substrate under every other data pillar.

You will:

Kill the single-gatekeeper deploy model and replace it with a real deploy regime where production access carries responsibility.

Define gold-certification by code, so quality is promoted by written criteria, never declared by hand.

Build and run the platform as a portfolio of data products with clear contracts to the domain teams.

Own data security and governance: take the our key data products to general availability, enforce least privilege, and retire ad-hoc database credentials.

Stand up observability so we can see what the platform is doing, not guess.

What You'll Work On

Building the deploy regime that lets every engineer ship without a single point of failure.

Implementing the medallion architecture with gold certified by code, and separating pipeline state from food facts.

Enable access to data products through MCPs and other AI friendly interfaces that honor our security and governance posture.

Building operational excellence within our data practices at WISEcode .

Building a reusable/plugable entity-resolution service to replace one-off fixes.

Running the platform as automated, agentic workflows with your own development harnesses.

Managing data engineering ICs.

What Success Looks Like

Within the first several months, you have:

Removed the deploy bottleneck and unblocked the rest of the data org.

Defined and enforced gold-certification-by-code.

Implemented our core promotion and release operation.

Given the org visibility into platform health it did not have before.

Over time, success means WISEcode data is queryable by a person or an agent without tribal knowledge, trustworthy enough to survive an audit, and shipped on a cadence measured in days.

AI Skills and Mindset