Note: The job is a remote job and is open to candidates in USA. WISEcode is an AI-native company building a food intelligence platform. The VP of Data Platform & Engineering will own data products, engineering delivery, data security, governance, and the platform infrastructure that enables trustworthy, automated, and agentic data workflows.
Responsibilities
- 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
- 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
- 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
Skills
- 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
- 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
Benefits
- Meaningful equity
Company Overview
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