Founding Data Engineer
Artificial Intelligence
Big Data
Data
Data Analysis
Data Analytics
Data Architecture
Data Engineer
Data Engineering
Data Governance
Data Integration
Data Integration Tools
Data Lineage
Data Management
Data Pipeline
Data Platform
Data Processing
Data Security
Database
Databases
ETL
Graph Database
Informatica
Integration
Knowledge Graph
Rag Architectures
SQL
Vector Databases
Job Description
Alldus International Consulting is hiring a Founding Data Engineer in San Francisco, CA (onsite). This role will design and build backend data systems, data pipelines, connector frameworks, and graph-based knowledge models that support Agentic AI applications.
Responsibilities
- Build reliable, scalable data ingestion and transformation pipelines spanning structured, semi-structured, and unstructured sources.
- Develop and maintain connector frameworks to integrate enterprise systems, including ERPs, PLMs, CRMs, legacy platforms, email, Excel, documents, and web APIs.
- Design and enhance a Data Fabric layer, including knowledge graphs enriched with ontologies, metadata, and relationships.
- Prepare, enrich, normalize, and vectorize enterprise data for AI and LLM workflows, including RAG, summarization, and intelligent alerting.
- Establish and maintain data contracts, access layers, lineage, governance, and data quality processes.
- Develop secure APIs that allow services, AI agents, and users to access and query enriched semantic data.
- Partner with ML and LLM teams to deliver high-quality enterprise data for model training, tuning, and AI applications.
- Build robust systems that support schema evolution, observability, scalability, and dependable production operations.
Requirements
- Minimum 5 years of experience designing, building, and operating large-scale data infrastructure in production environments.
- Strong hands-on experience with ingestion frameworks such as Kafka, Airbyte, Meltano, or Fivetran, along with orchestration tools such as Airflow, Dagster, or Prefect.
- Proven experience with structured, semi-structured, and unstructured data, including PDFs, Excel, emails, logs, CSV, and web APIs.
- Experience with columnar data stores, object storage, and modern lakehouse technologies including Iceberg, Delta, or Parquet.
- Strong knowledge of knowledge graphs and semantic modeling, including experience with Neo4j, RDF, Gremlin, or PuppyGraph.
- Experience designing and developing GraphQL and REST APIs, plus scalable developer-friendly data access layers.
- Strong understanding of data governance principles, including RBAC, ABAC, data contracts, lineage, and data quality.
- Experience with vector databases and embedding pipelines, including 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.
Technologies
- Kafka; Airbyte; Meltano; Fivetran
- Airflow; Dagster; Prefect
- Iceberg; Delta; Parquet
- Neo4j; RDF; Gremlin; PuppyGraph
- GraphQL; REST APIs
- RBAC; ABAC
- Weaviate; Qdrant; Pinecone
- RAG pipelines; LLM fine-tuning; Linked Data; W3C standards
Location
San Francisco, CA (onsite)
Compensation
USD 150,000 - 200,000 per yearly
Benefits
- Salary: $150k to $200k
- Equity
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