Job Description
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Company Overview
An early-stage technology company backed by 8VC is building a multi-tenant, AI-native platform designed to transform enterprise data into actionable intelligence through semantic enrichment, intelligent agents, and governed interoperability.
Key Responsibilities
- Build highly reliable and scalable data ingestion and transformation pipelines across structured, semi-structured, and unstructured data sources.
- Develop and maintain connector frameworks for ingesting data from enterprise systems, including ERPs, PLMs, CRMs, legacy data stores, email, Excel files, documents, and other sources.
- Design and maintain the data fabric layer, including knowledge graphs using technologies such as Neo4j or PuppyGraph.
- Enrich data with ontologies, metadata, and relationships to create connected semantic data models.
- Normalize and vectorize data for downstream AI and LLM workflows, including retrieval-augmented generation (RAG), summarization, and alerting.
- Create and manage data contracts, access layers, data lineage, and governance mechanisms.
- Build and expose secure APIs that enable downstream services, agents, and users to query enriched semantic data.
- Collaborate with ML and LLM engineering teams to provide high-quality enterprise data for model training and tuning pipelines.
- Develop scalable systems capable of handling messy, fragmented, and continuously evolving enterprise data.
- Apply observability, scalability, security, and schema-evolution best practices throughout the data infrastructure.
Required Qualifications
- 5+ years of experience building large-scale data infrastructure in production environments.
- Deep experience with data ingestion frameworks such as Kafka, Airbyte, Meltano, or Fivetran.
- Experience with data pipeline orchestration tools such as Airflow, Dagster, or Prefect.
- Experience processing unstructured data formats, including PDFs, Excel files, emails, logs, CSVs, and web APIs.
- Experience working with columnar data stores, object storage, and lakehouse formats such as Iceberg, Delta, and Parquet.
- Strong background in knowledge graphs or semantic modeling, including technologies such as Neo4j, RDF, Gremlin, or PuppyGraph.
- Familiarity with GraphQL and RESTful APIs and experience designing developer-friendly data access layers.
- Experience implementing data governance practices, including RBAC, ABAC, data contracts, lineage, and data quality checks.
- Strong understanding of scalable data architecture, observability, and schema evolution.
- Ability to work independently in an early-stage environment and take ownership of infrastructure initiatives.
- Strong problem-solving skills and the ability to work with ambiguous and complex data models.
Mindset & Culture
- Thinks systematically and focuses on modeling real-world systems rather than simply processing data.
- Comfortable navigating ambiguous data models and building systems from the ground up.
- Passionate about enabling AI systems with real-world enterprise data.
- Pragmatic about scalability, observability, and schema evolution.
- Values autonomy, high trust, and meaningful ownership of infrastructure.
Bonus Skills
- Experience with vector databases such as Weaviate, Qdrant, or Pinecone.
- Experience building or contributing to enterprise connector ecosystems.
- Knowledge of ontology versioning, graph diffing, or semantic schema alignment.
- Familiarity with data fabric patterns, including Palantir Ontology, Linked Data, and W3C standards.
- Experience fine-tuning LLMs or enabling RAG pipelines using enterprise knowledge.
- Experience enforcing data access policies using technologies such as OPA, Keycloak, or Snowflake row-level security.
- Experience designing embedding pipelines and AI-ready data processing workflows.
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