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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 - Deep experience with ingestion frameworks (Kafka, Airbyte, Meltano, Fivetran) and data pipeline orchestration (Airflow, Dagster, Prefect) - Comfortable processing unstructured data formats: PDFs, Excel, emails, logs, CSVs, web APIs - Experience working with columnar stores, object storage, and lakehouse formats (Iceberg, Delta, Parquet) - Strong background in knowledge graphs or semantic modeling (e.g. Neo4j, RDF, Gremlin, Puppygraph) - Familiarity with GraphQL, RESTful APIs, and designing developer-friendly data access layers - Experience implementing data governance: RBAC, ABAC, data contracts, lineage, data quality checks Mindset & Culture Fit: - You’re a system thinker: you want to model the real world, not just process it - Comfortable navigating ambiguous data models and building from scratch - Passionate about enabling AI systems with real-world, messy enterprise data - Pragmatic about scalability, observability, and schema evolution - Value autonomy, high trust, and meaningful ownership over infrastructure Bonus Skills - Prior work with vector DBs (e.g. Weaviate, Qdrant, Pinecone) and embedding pipelines - Experience building or contributing to enterprise connector ecosystems - Knowledge of ontology versioning, graph diffing, or semantic schema alignment - Familiarity with data fabric patterns (e.g. Palantir Ontology, Linked Data, W3C standards) - Familiar with fine-tuning LLMs or enabling RAG pipelines using enterprise knowledge - Experience enforcing data access policy with tools like OPA, Keycloak, Snowflake row-level security WHY THIS ROLE MATTERS Agents are only as smart as the data they operate on. This role builds the foundation — the semantic, governed, connected substrate — that