Data Architect

Job Title :- Data Architect

Employment Type :- W2

Duration :- Long Term

Visa Type :- All Visa applicable which are ready for W2

Location :- Dallas TX or Kansas City MO (Hybrid, 2-3 days a week)

Key Skills –DB Architecture, Azure, Cosmos DB, VectorDBs, PostgreSQL

Job Description

Required Qualifications

• 10+ years of experience in data engineering, with at least 3 years focused on Generative AI technologies.
• Expertise in designing and architecting production-ready enterprise grade data pipelines.
• Strong knowledge of vector databases, graph databases, NoSQL, Document DBs , including design, implementation, and optimization. (e.g. AWS open search or GCP Vertex AI, neo4j etc. Mongo, Dynamo, CosmosDB etc.)
• Expertise in designing, loading, and querying NoSQL, graph databases.
• Extensive experience in processing and leveraging unstructured data for data applications.
• Strong programming skills in Python and knowledge of deep learning frameworks such as PyTorch or TensorFlow.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Excellent communication skills and ability to explain complex technical concepts to both technical and non-technical stakeholders.

Job Responsibilities

• Hands on experience designing and developing end-to-end data pipelines, from data ingestion to pipeline deployment and monitoring.
• Lead the architecture and implementation of complex agentic frameworks, ensuring best practices in software engineering and data integrations.
• Collaborate with cross-functional teams (Technology, Business, Platform etc.) to integrate data solutions into existing data products and services.
• Stay at the forefront of GenAI advancements and incorporate new technologies and methodologies into our systems.
• Mentor and guide junior data engineers and architects in best practices for development.
• Design and implement optimized RAG architectures and pipelines.
• Design and implement strategies for handling unstructured data in pipelines.
• Design and implement graph database solutions for complex data relationships.
• Integrate data pipelines with Snowflake data warehouse for efficient data processing and storage.
• Build POCs to compare and evaluate various emerging technologies around data and AI.
• Create prototypes to establish patterns.