Data Architect - AI & Knowledge Platform

About WebLife:

For over 16 years, WebLife has been a leader in the e-commerce industry, operating category-defining stores and becoming one of the largest distributors of mailboxes in the U.S. We have a long history of building data-driven operations and best-in-class teams.

We are now building an AI-native operating system for how small and mid-sized businesses run their operations a product that turns AI from one-shot chat into a durable, persistent system with real institutional memory. This is a small, high-energy founding team that combines the creative speed of a startup with the stability and resources of an established company.

About the Role:

As our founding Data Architect, you will design and build the data and knowledge systems at the core of our AI-native platform. This is a hands-on architecture role where you will own application data end-to-endfrom modeling, storage, synchronization, and governance through to production operations.

Our platform combines traditional application data with AI-native memory systems, including semantic retrieval, RAG pipelines, embeddings, context graphs, and knowledge structures that enable AI to reason over organizational memory.

You will define the data foundations for an offline-capable, cloud-synced product, ensuring scalability, security, auditability, and long-term flexibility across relational, vector, graph, and future data stores.

We invest heavily in getting the data model and system boundaries right, then execute rapidly. In our small founding team, you will lead by doingsetting technical direction while actively building systems that reach customers.

Key Responsibilities:Data Architecture & Governance:

• Own the end-to-end architecture of application data across cloud, local, and offline-capable environments.

• Define database governance standards including schemas, primary keys, migrations, backward compatibility, and change management.

• Design secure multi-tenant data models, tenant isolation strategies, and data lifecycle management.

• Architect for extensibility across relational, vector, graph, search, and future data stores.

Knowledge Systems & AI Memory:

• Design and implement organizational memory systems including knowledge records, semantic chunks, embeddings, metadata, relationships, and audit history.

• Build and operate RAG pipelines including ingestion, chunking, embedding, indexing, retrieval, ranking, and evaluation.

• Design knowledge structures such as context graphs, hierarchies, and retrieval models that improve AI reasoning.

• Establish governance mechanisms for knowledge freshness, approval workflows, lifecycle states, and auditability.