Data Architect- GenAI

VBeyond Corporation; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

VBeyond Corporation is seeking an experienced Data Architect in New York to design scalable data architectures and data models that support GenAI applications and AI-driven knowledge platforms. You will lead end-to-end data engineering, from ingestion to enrichment, across structured, semi-structured, and unstructured data.

The role emphasizes graph database design using Neo4j, Python-based data services with FastAPI, and tight integration of relational and vector data stores with governance,

Qualifications

• Experience designing scalable data architectures and data models.
• Hands-on development of ETL/ELT pipelines for diverse data.
• Build and optimize graph data models using graph databases.
• Develop Python-based data services and APIs for AI workloads.
• Integrate relational, graph, and vector data stores with accuracy.
• Collaborate with GenAI teams to meet ingestion and retrieval needs.

Responsibilities

• Design scalable data architectures, data models, and ETL/ELT pipelines.
• Build data pipelines to ingest documents and data into AI platforms.
• Develop graph data models using Neo4j or equivalent.
• Develop Python-based data pipelines and services with FastAPI.
• Work with PostgreSQL, pgVector, embeddings, and vector search integrations.
• Implement data quality, lineage, metadata, and reprocessing mechanisms.
• Collaborate with GenAI and AI agent teams to meet data requirements.

Skills

Data architecture

Data modeling

ETL/ELT pipelines

Graph databases

Python

API development

Vector search / embeddings

GenAI / AI readiness

Tools

Neo4j

PostgreSQL

pgVector

FastAPI

REST APIs

Job description

We are looking for an experienced Data Architect with strong hands-on expertise in data architecture, data engineering, graph databases, Python-based data services, and AI-ready data pipelines. The ideal candidate will design and develop scalable data solutions that support GenAI applications, knowledge platforms, and AI agent consumption.

Key Responsibilities & Required Skills:

• Design scalable data architectures, data models, and ETL/ELT pipelines for structured, semi-structured, and unstructured data.
• Build data pipelines to extract, transform, enrich, chunk, and ingest documents and data into AI and knowledge platforms.
• Develop graph data models using Neo4j or equivalent graph databases, including nodes, relationships, properties, indexing, and graph queries.
• Develop Python-based data pipelines and services using FastAPI and REST APIs.
• Work with PostgreSQL, pgVector, embeddings, and vector search, integrating relational, graph, and vector data stores.
• Implement data integration, validation, metadata management, lineage, data quality, error handling, and reprocessing mechanisms.
• Collaborate with GenAI and AI agent teams to meet data ingestion, retrieval, and contextual data requirements.

Preferred Skills:

• Knowledge of GenAI, RAG, embeddings, document processing, and AI knowledge bases.
• Experience with GraphQL, graph-based APIs, and data access layers.
• Exposure to Google Gemini, Google ADK, and A2A frameworks.
• Familiarity with cloud-native data platforms and AI application architectures.
• Experience with GitHub Copilot or similar AI-assisted development tools.
• Enterprise data architecture experience, preferably in banking or financial services (BFSI), with an understanding of data governance, security, and privacy.