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About the job

Our client, a growing AI and Data organization, is hiring a Founding Data Engineer to join the team. The successful candidate will help transform enterprise data into actionable intelligence by building the backend systems, data pipelines, connector frameworks and graph-based knowledge models that power their Agentic AI applications.

Responsibilities

Build reliable, scalable data ingestion and transformation pipelines across structured, semi-structured and unstructured data sources.

Develop and maintain connector frameworks that integrate enterprise systems, including ERPs, PLMs, CRMs, legacy platforms, email, Excel, documents and web APIs.

Design and enhance the Data Fabric layer, including knowledge graphs enriched with ontologies, metadata and relationships.

Prepare, enrich, normalize and vectorize enterprise data for AI and LLM applications, including RAG, summarization and intelligent alerting.

Establish and maintain data contracts, access layers, lineage, governance and data quality processes.

Develop secure APIs that enable services, AI agents and users to access and query enriched semantic data.

Partner with ML and LLM teams to provide high-quality enterprise data for model training, tuning and AI applications.

Build robust systems that support schema evolution, observability, scalability and reliable production operations.

Skillset

Minimum of 5 years of experience designing, building and operating large-scale data infrastructure in production environments.

Strong hands-on experience with data ingestion frameworks such as Kafka, Airbyte, Meltano or Fivetran, alongside orchestration tools such as Airflow, Dagster or Prefect.

Proven experience working with structured, semi-structured, and unstructured data, including PDFs, Excel, emails, logs, CSVs and web APIs.

Experience with columnar data stores, object storage and modern lakehouse technologies such as Iceberg, Delta or Parquet.

Strong understanding of knowledge graphs and semantic modelling, with experience using platforms such as Neo4j, RDF, Gremlin or PuppyGraph.

Experience designing and developing GraphQL and REST APIs, as well as scalable and developer-friendly data access layers.

Strong knowledge of data governance principles, including RBAC, ABAC, data contracts, lineage and data quality.

Experience with vector databases and embedding pipelines, including technologies, such as Weaviate, Qdrant or Pinecone, is highly desirable.

Familiarity with enterprise connector ecosystems, ontology versioning, graph diffing and semantic schema alignment is an advantage.

Exposure to RAG pipelines, LLM fine-tuning, Linked Data, W3C standards or modern data fabric architectures is a plus.

Benefits

Salary: $150k - $200k.

Equity.