VP, Data Platform & Engineering

WISEcode

About The Position

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. 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 WISEcode is an AI-native company, and this role sets the bar for what that means in engineering. You will: Build automated, agentic pipelines rather than only manual runbooks. Operate with your own AI development harness and raise the standard for your team an the wider organization. Design the platform so employees get the full power of frontier models without exposing the corpus. Treat probabilistic systems with architectural guardrails, not trust in behavior. We are not looking for someone who treats AI as novelty. We are looking for someone who treats it as infrastructure.

Requirements

10+ years in data engineering, data platform, or infrastructure, including team leadership.
A track record of building data products and teams from zero to one.
Deep command of data contracts, medallion or equivalent quality architecture, and promotion discipline.
Strong data-security judgment: least privilege, trust boundaries, and access control.
Fluency with AI-assisted and agentic engineering as a primary way of working.
Strong SQL and systems-thinking across UI, API, database, and infrastructure boundaries.
Ability to operate in an ambiguous, fast-moving startup environment.

Nice To Haves

Experience with MCPs, capability layers, or API-first data access patterns.
Experience with observability tooling and production diagnostics.
Experience with Postgres, DuckDB or MotherDuck, and cloud infrastructure.
Experience building internal developer tooling or data-product platforms.

Responsibilities

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.
Set the AI-native engineering standard for the data organization.
Deliver internal data products as authenticated capabilities and contracts.
Manage the shipment motion for data records from raw input to scored, published output, automated and agentic.
Establish the trust boundary, enforce least-privilege access, and uphold corpus-stewardship doctrine.
Kill the single-gatekeeper deploy model and replace it with a real deploy regime where production access carries responsibility.
Define gold-certification by code.
Build and run the platform as a portfolio of data products with clear contracts to domain teams.
Take key data products to general availability, enforce least privilege, and retire ad-hoc database credentials.
Stand up observability to monitor platform activity.
Build the deploy regime that allows engineers to ship without a single point of failure.
Implement the medallion architecture with gold certified by code, separating pipeline state from food facts.
Enable access to data products through MCPs and other AI friendly interfaces that honor security and governance posture.
Build operational excellence within data practices.
Build a reusable/plugable entity-resolution service.
Run the platform as automated, agentic workflows with development harnesses.
Manage data engineering ICs.
Build automated, agentic pipelines.
Operate with an AI development harness and raise the standard for the team and organization.
Design the platform to leverage frontier models without exposing the corpus.
Treat probabilistic systems with architectural guardrails.