Principal AI & Data Architect (Snowflake, Semantic Modeling, Agentic AI)

TalentOla•Charlotte, NC

8d•Remote

About The Position

The Principal AI & Data Architect will define and lead enterprise data and AI architecture. The role designs a Snowflake-centric data platform with a strong semantic layer and builds foundations for agentic AI systems that reason over trusted, governed enterprise data. It is a hands-on technical leadership position partnering with business, engineering, and security teams to turn data into reliable, AI-ready products. The architect owns end-to-end Snowflake platform architecture, enterprise semantic modeling, and agentic AI and generative AI architecture. The role also establishes governance and security controls, provides technical guidance and mentorship, influences executive stakeholders, and evaluates emerging technologies. The position is remote in the US, aligned to the Eastern time zone, and is described as a contractor engagement lasting one year or longer.

Requirements

12+ years of experience in data engineering, data architecture, or analytics.
5+ years of experience in architect-level roles.
3+ years of hands-on Snowflake architecture experience at enterprise scale.
Deep expertise in data modeling, including 3NF, dimensional modeling, and Data Vault, and in semantic-layer design.
Proven delivery of production GenAI/agentic AI solutions, including RAG, tool-calling agents, and multi-agent workflows.
Strong SQL and Python skills.
Experience with dbt, Airflow or Dagster, Kafka, and CI/CD tools and practices, including Git and Terraform.
Experience with at least one major cloud platform: AWS, Azure, or GCP.
Solid understanding of data governance, security, and privacy frameworks.
Excellent communication, stakeholder management, and leadership skills.
Contractor engagement with a duration of one year or longer; remote in the US and aligned to the Eastern time zone.

Nice To Haves

SnowPro Advanced certification, with Architect preferred; Data Engineer and Data Scientist certifications are also noted.
Cloud architect certifications.
Experience with knowledge graphs or ontologies, including RDF/OWL or Neo4j, and graph-RAG.
Experience with vector databases and embedding strategies.
Background in MLOps and feature stores.
Industry experience in BFSI, healthcare, retail, or manufacturing.
Contributions to architecture blogs, talks, or open-source projects.

Responsibilities

Own the end-to-end architecture of the Snowflake Data Cloud, spanning ingestion, storage, transformation, serving, and consumption.
Design scalable and cost-efficient solutions using Snowflake capabilities including Snowpipe, Streams and Tasks, Dynamic Tables, Snowpark, Iceberg tables, Secure Data Sharing, and Marketplace.
Define standards for performance tuning, warehouse sizing, clustering, FinOps, and cost governance.
Lead modernization and migration from legacy platforms including Teradata, Oracle, Hadoop, and SQL Server to Snowflake.
Architect enterprise semantic layers, including business metrics, dimensions, hierarchies, and entity relationships.
Design dimensional (Kimball), Data Vault 2.0, and canonical/domain models, and map them to the semantic layer.
Implement Snowflake Semantic Views and Cortex Analyst semantic models, and integrate with tools such as dbt Semantic Layer/MetricFlow, AtScale, Cube, or LookML.
Establish ontologies, knowledge graphs, and business glossaries to ensure consistent enterprise meaning.
Own metadata, lineage, and data contracts across domains using data mesh or data product approaches.
Design agentic AI architectures covering multi-agent orchestration, tool use, planning, memory, and guardrails.
Build solutions using Snowflake Cortex, including Cortex Agents, Cortex Analyst, Cortex Search, and AI functions, and integrate with LLMs and frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or CrewAI.
Implement RAG and text-to-SQL patterns grounded in the semantic layer to provide accurate, explainable answers.
Use MCP and API/tool integrations to expose governed data and actions to agents.
Define LLMOps/AgentOps practices for evaluation, observability, prompt and version management, human-in-the-loop processes, and cost control.
Establish Responsible AI practices addressing bias, hallucination, privacy, and auditability.
Implement RBAC, row- and column-level security, dynamic masking, tagging, and compliance controls for GDPR, HIPAA, SOC 2, and PCI.
Partner with data governance teams on data quality, stewardship, and master data management.
Provide architectural guidance, conduct design reviews, and mentor data engineers, ML engineers, and analysts.
Influence executive stakeholders and translate business needs into roadmaps and ROI-backed proposals.
Evaluate emerging technologies and produce reference architectures, proofs of concept, and best practices.