DS
Principal AI & Data Architect (Snowflake, Semantic Modeling, Agentic AI)
Damco Solutions Inc
- Location not stated
- Remote
- Staff / Principal
- 13 hours ago
- AI
- Snowflake
- Data Architecture
- FinOps
- Teradata
- Oracle
- Hadoop
- Data Modeling
- Vault
- Cortex
- dbt
- Machine Learning
- LangGraph
- LangChain
- LlamaIndex
- AutoGen
- CrewAI
- RAG
- SQL
- MCP
- Model Context Protocol
- RBAC
- GDPR
- HIPAA
- SOC2
- Python
- Airflow
- Dagster
- CI/CD
- Git
- Terraform
- AWS
- Azure
- GCP
- Neo4j
- MLOps
13 hours ago
Experience: Minimum 12 years
1 yr project plus extension.
Remote in US in EST zone
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.
- 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).
- 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.
- 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.
- 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.
- 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
Principal AI & Data Architect (Snowflake, Semantic Modeling, Agentic AI) · Damco Solutions Inc