over 1 year ago
Responsibilities
- Build scalable and reliable ingestion and transformation pipelines for structured, semi-structured, and unstructured data.
- Develop and maintain connector frameworks for enterprise systems, legacy data stores, email, spreadsheets, documents, and other sources.
- Design and maintain the data fabric, including knowledge graphs, ontologies, metadata, and relationships.
- Normalize and vectorize data for retrieval, summarization, alerting, and other AI/LLM workflows.
- Create data contracts, access layers, lineage systems, governance mechanisms, and data quality checks.
- Build secure APIs for downstream services, agents, and users to query enriched semantic data.
- Collaborate with ML and LLM teams to supply enterprise data for model training and tuning pipelines.
Requirements
- 5+ years of experience building large-scale data infrastructure in production environments.
- Deep experience with ingestion frameworks including Kafka, Airbyte, Meltano, and Fivetran.
- Experience with pipeline orchestration using Airflow, Dagster, or Prefect.
- Experience processing unstructured data such as PDFs, Excel files, emails, logs, CSVs, and web APIs.
- Experience with columnar stores, object storage, and lakehouse formats including Iceberg, Delta, and Parquet.
- Strong knowledge of knowledge graphs or semantic modeling, including technologies such as Neo4j, RDF, Gremlin, or Puppygraph.
- Familiarity with GraphQL, RESTful APIs, and developer-friendly data access layers.
- Experience implementing RBAC, ABAC, data contracts, lineage, and data quality checks.
- Prior work with vector databases such as Weaviate, Qdrant, or Pinecone is a bonus.
- Experience with enterprise connector ecosystems, ontology versioning, graph diffing, semantic schema alignment, data fabric patterns, LLM fine-tuning, RAG pipelines, or enterprise data access policy tools is a bonus.
Benefits
- Full-time founding-team role based in the San Francisco Bay Area.
- Competitive salary and early-stage equity.
- Meaningful ownership, autonomy, and the opportunity to build infrastructure from scratch.
Tech Stack
Categories
Data Engineering
About Fabrion
Fabrion builds enterprise AI infrastructure centered on agent-based systems, knowledge graphs, and multi-tenant governance for mission-critical workflows. The privately held company operates an Agentic AI Lab to train and evaluate specialized models, offering software and platform services to large organizations; founded in 2025 and headquartered in San Francisco.