DPR Solutions Inc

Job Description Job Description Interviews: two internal rounds, one customer Experience: Minimum 12 years 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.