Data/ETL Engineer (Founding Team)

Location: San Francisco Bay Area

Type: Full-Time

Compensation: Competitive salary + early-stage equity

Backed by 8VC, we're building a world-class team to tackle one of the industry’s most critical infrastructure problems.

About the Role

We’re building a multi-tenant, AI-native platform where enterprise data becomes actionable through semantic enrichment, intelligent agents, and governed interoperability. At the heart of this architecture lies our Data Fabric — an intelligent, governed layer that turns fragmented and siloed data into a connected ontology ready for model training, vector search, and insight-to-action workflows.

We're looking for engineers who enjoy hard data problems at scale: messy unstructured data, schema drift, multi-source joins, security models, and AI-ready semantic enrichment. You’ll build the backend systems, data pipelines, connector frameworks, and graph-based knowledge models that fuel agentic applications.

If you've worked on streaming unstructured pipelines, built connectors into ugly legacy systems, or mapped knowledge graphs that scale — this role will feel like home.

Responsibilities

• Build highly reliable, scalable data ingestion and transformation pipelines across structured, semi-structured, and unstructured data sources
• Develop and maintain a connector framework for ingesting from enterprise systems (ERPs, PLMs, CRMs, legacy data stores, email, Excel, docs, etc.)
• Design and maintain the data fabric layer — including a knowledge graph (Neo4j or Puppygraph) enriched with ontologies, metadata, and relationships
• Normalize and vectorize data for downstream AI/LLM workflows — enabling retrieval-augmented generation (RAG), summarization, and alerting
• Create and manage data contracts, access layers, lineage, and governance mechanisms
• Build and expose secure APIs for downstream services, agents, and users to query enriched semantic data
• Collaborate with ML/LLM teams to feed high-quality enterprise data into model training and tuning pipelines

What We’re Looking For

Core Experience:

• 5+ years building large-scale data infrastructure in production environments