Data Engineer

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 like ERPs, PLMs, and CRMs
• Design and maintain the data fabric layer including knowledge graphs enriched with ontologies and metadata
• Normalize and vectorize data for downstream AI/LLM workflows such as RAG and summarization
• Create and manage data contracts, access layers, lineage, and governance mechanisms
• Build and expose secure APIs for downstream services and agents
• Collaborate with ML/LLM teams to feed high-quality enterprise data into model training pipelines

Requirements

• 5+ years building large-scale data infrastructure in production environments
• Deep experience with ingestion frameworks like Kafka, Airbyte, Meltano, or Fivetran
• Experience with pipeline orchestration tools such as Airflow, Dagster, or Prefect
• Proficiency processing unstructured data formats including PDFs, Excel, and logs
• Experience with columnar stores, object storage, and lakehouse formats like Iceberg or Delta
• Strong background in knowledge graphs or semantic modeling (Neo4j, RDF, Gremlin, or Puppygraph)
• Familiarity with GraphQL, RESTful APIs, and designing data access layers
• Experience implementing data governance including RBAC, ABAC, and data contracts

Preferred Qualifications

• Prior work with vector databases such as Weaviate, Qdrant, or Pinecone
• Experience building or contributing to enterprise connector ecosystems
• Knowledge of ontology versioning or semantic schema alignment
• Familiarity with data fabric patterns and W3C standards
• Experience enabling RAG pipelines or fine-tuning LLMs
• Experience enforcing data access policy with tools like OPA or Keycloak