Job Description
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Company Overview
An early-stage technology company backed by leading venture investors, focused on solving complex enterprise data infrastructure challenges. The company is developing an AI-native, multi-tenant platform that transforms fragmented enterprise data into actionable, connected information through semantic enrichment, intelligent agents, and governed interoperability.
Responsibilities
- Build highly reliable and scalable data ingestion and transformation pipelines across structured, semi-structured, and unstructured data sources.
- Develop and maintain a connector framework for ingesting data from enterprise systems, including ERPs, PLMs, CRMs, legacy data stores, email, spreadsheets, documents, and other sources.
- Design and maintain a data fabric layer incorporating knowledge graphs, ontologies, metadata, and relationships.
- Normalize and vectorize enterprise 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 allow downstream services, agents, and users to query enriched semantic data.
- Collaborate with ML and LLM teams to provide high-quality enterprise data for model training and tuning pipelines.
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.
- Comfortable 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, or 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 capabilities such as RBAC, ABAC, data contracts, lineage, and data quality checks.
- Ability to model complex real-world data systems rather than simply processing individual datasets.
- Comfortable navigating ambiguous data models and building infrastructure from the ground up.
- Strong interest in enabling AI systems with complex and unstructured enterprise data.
- Practical understanding of scalability, observability, and schema evolution.
- Ability to work autonomously in an environment that emphasizes ownership and responsibility.
Bonus Qualifications
- Experience with vector databases such as Weaviate, Qdrant, or 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, including Palantir Ontology, Linked Data, or W3C standards.
- Experience fine-tuning LLMs or enabling RAG pipelines using enterprise knowledge.
- Experience enforcing data access policies using tools such as OPA, Keycloak, or Snowflake row-level security.
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