Data Architect - AI & Knowledge Platform

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. Our product transforms 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-end, including modeling, storage, synchronization, governance, and 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. As part of our small founding team, you will lead by doing, setting technical direction while actively building systems that reach customers.

This is a remote role open to candidates based in Sri Lanka.

KEY RESPONSIBILITIES

1. Data Architecture and 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.

1. Knowledge Systems and AI Memory

• Design and implement organizational memory systems, including knowledge records, semantic chunks, embeddings, metadata, relationships, and audit history.
• Build and operate RAG pipelines covering 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.
• Translate research-grade AI memory and retrieval concepts into pragmatic MVPs while avoiding over-engineering and preserving long-term architectural flexibility.

1. Data Platforms and Operations

• Build reliable event-driven and batch processing pipelines with strong guarantees around data quality, idempotency, and scalability.
• Own infrastructure, CI/CD, observability, monitoring, and production reliability for data services.
• Troubleshoot production issues, perform root-cause analysis, and drive continuous improvement.

4.. Collaboration and Leadership

• Partner with AI engineers, product teams, and stakeholders to translate ambiguous requirements into scalable production systems.
• Balance long-term architectural decisions with pragmatic MVP delivery.
• Mentor engineers and establish best practices as the platform scales.

REQUIREMENTS

1. Education and Experience

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field. A master’s degree is preferred.
• 57+ years of experience in Data Engineering, Platform Engineering, Backend Engineering, or related technical roles.
• 3+ years of experience owning data architecture or platforms end-to-end.
• Experience designing application databases beyond analytics or reporting systems.
• Experience working in startup or high-ownership environments is highly desirable.
• Experience working with international teams or clients is advantageous.

1. Data and Platform Expertise

• Strong expertise in PostgreSQL, including schema design, indexing, migrations, performance tuning, and operational reliability.
• Experience with offli…