Principal AI & Data Architect

Job Description:

About the Role

We are looking for a Principal AI & Data Architect to define and lead our enterprise data and AI architecture. You will design a Snowflake-centric data platform with a strong semantic layer, and build the foundations for agentic AI systems that reason over trusted, governed enterprise data. You will be a hands-on technical leader, partnering with business, engineering, and security teams to turn data into reliable, AI-ready products.

Key Responsibilities

Data Architecture & Snowflake Platform

• Own the end-to-end architecture of the Snowflake Data Cloud: ingestion, storage, transformation, serving, and consumption.
• Design scalable, cost-efficient solutions using Snowflake features such as Snowpipe, Streams & Tasks, Dynamic Tables, Snowpark, Iceberg tables, Secure Data Sharing, and Marketplace.
• Define standards for performance tuning, warehouse sizing, clustering, and FinOps/cost governance.
• Lead modernization and migration from legacy platforms (Teradata, Oracle, Hadoop, SQL Server) to Snowflake.

Semantic & Data Modeling

• 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 like dbt Semantic Layer/MetricFlow, AtScale, Cube, or LookML.
• Establish ontologies, knowledge graphs, and business glossaries to ensure consistent meaning across the enterprise.
• Own metadata, lineage, and data contracts across domains (data mesh / data product approach).

Agentic AI & GenAI Architecture

• Design agentic AI architectures: multi-agent orchestration, tool use, planning, memory, and guardrails.
• Build solutions on Snowflake Cortex (Cortex Agents, Cortex Analyst, Cortex Search, 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 for accurate, explainable answers.
• Leverage MCP (Model Context Protocol) and API/tool integrations to expose governed data and actions to agents.
• Define LLMOps/AgentOps practices: evaluation, observability, prompt/version management, human-in-the-loop, and cost control.
• Establish Responsible AI practices covering bias, hallucination, privacy, and auditability.

Governance, Security & Leadership

• Implement RBAC, row/column-level security, dynamic masking, tagging, and compliance controls (GDPR, HIPAA, SOC 2, PCI).
• Partner with data governance on quality, stewardship, and master data management.
• Provide architectural guidance, design reviews, and mentorship to data engineers, ML engineers, and analysts.
• Influence executive stakeholders; translate business needs into roadmaps and ROI-backed proposals.
• Evaluate emerging technologies and produce reference architectures, POCs, and best practices.

Required Qualifications

• 12+ years in data engineering, data architecture, or analytics, with 5+ years in architect-level roles.
• 3+ years of hands-on Snowflake architecture at enterprise scale (SnowPro Advanced: Architect preferred).
• Deep expertise in data modeling (3NF, dimensional, Data Vault) and semantic layer design.
• Proven delivery of GenAI/agentic AI solutions in production (RAG, tool-calling agents, multi-agent workflows).
• Strong SQL and Python; experience with dbt, Airflow/Dagster, Kafka, and CI/CD (Git, Terraform).
• Experience on at least one major cloud (AWS, Azure, or GCP).
• Solid understanding of data governance, security, and privacy frameworks.
• Excellent communication, stakeholder management, and leadership skills.

Preferred Qualifications

• SnowPro Advanced certifications (Architect, Data Engineer, Data Scientist) and cloud architect certifications.
• Experience with knowledge graphs/ontologies (RDF/OWL, Neo4j) and graph-RAG.
• Experience with vector databases and embedding strategies.
• Background in MLOps and feature stores.
• Industry experience in [BFSI / Healthcare / Retail / Manufacturing].
• Contributions to architecture blogs, talks, or open-source.