Snowflake AI Data Engineer- Local to NY- Fulltime Role

Location: NYC, NY (Onsite)
Duration: Full-time
Visa sponsorship is not available; only Green Card holders and US Citizens may apply.
In-person interview required in NYC this Friday (one round).

About the role

As the onsite technical lead, you will drive Snowflake-native AI, GenAI, and advanced data engineering on AWS Cloud. This role requires deep expertise in Snowflake, Cortex AI, Snowpark (Python), and Talend ETL, with ownership of delivering AI/ML and LLM-powered solutions directly within the Snowflake ecosystem while coordinating with offshore teams.

Responsibilities

• Design and implement Cortex-driven AI solutions using LLM capabilities (COMPLETE, CHAT, SUMMARIZE), embeddings (EMBED_TEXT), vector search, and RAG architectures, enabling AI copilots, semantic search, and intelligent analytics on enterprise data.
• Own Snowflake platform architecture (schemas, views, streams, tasks, Snowpipes) and enforce performance optimization best practices (warehouse sizing, clustering, query tuning).
• Implement secure data access using RBAC, masking, and row/column-level controls.
• Lead data engineering and integration pipelines (batch and near real-time) using Talend, Qlik Replicate, and Snowpipe, ensuring data quality, reconciliation, and schema evolution, and enabling ingestion from diverse source systems.
• Guide SQL, Python, and ELT transformations, and enable AI/ML workflows using Snowpark and integration with platforms such as Databricks, SageMaker, and Azure ML.
• Deliver AI-driven use cases such as claims triage, fraud detection, risk scoring, premium leakage, and pricing analytics, leveraging Snowflake-native AI capabilities and Python-based data science frameworks.
• Support CI/CD-driven deployments and automation.

Requirements

• Strong experience in Snowflake, Cortex AI, Snowpark (Python), Talend ETL, and AWS Cloud.
• Hands-on expertise in LLMs, embeddings, vector search, and RAG architectures within Snowflake.
• Proven leadership in Snowflake architecture, performance optimization, and scalable data platforms.
• Deep experience in data engineering, ELT pipelines, and real-time/batch data integration.
• Experience enabling AI/ML workloads and feature engineering within Snowflake environments.
• Strong knowledge of data security frameworks (RBAC, masking, row/column-level controls).
• Domain experience in insurance or financial services.
• Advanced proficiency in SQL and Python, with ability to lead engineering teams.