Demo

Data Architect

Highbrow LLC
Kansas, MO Full Time
POSTED ON 4/10/2025
AVAILABLE BEFORE 5/2/2025

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.
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