Snowflake Data engineer

Overview

Owns the architecture, build, and optimization of the Snowflake platform , including Cortex AI, vector search, and integrations with systems such as SQL Server and Salesforce HCM. Ensures secure, efficient, and insight-driven data environments through automation and governance, with a focus on enabling AI-powered data products.

Key Responsibilities

• Design and deploy Snowflake warehouses, databases, and AI capabilities (Cortex AI, vector search, summarization, forecasting) for analytics workloads.
• Automate provisioning, secrets management, schema deployment, and scaling using Terraform and GitHub Actions.
• Implement cost governance through usage tracking, dashboards, resource monitors, and query profiling.
• Integrate Snowflake with dbt, SQL Server, Salesforce HCM, Databricks, and BI tools (e.g., Sigma, Power BI) to enable unified AI/ML pipelines.
• Enforce data security through RBAC roles, dynamic masking, and efficient compute management (auto-suspend/resume).
• Partner with data, ML, finance, and business teams to build and optimize AI-driven data products and vector-based applications.
• Implement external tables and functions for hybrid analytics and data federation across platforms.
• Optimize performance using query acceleration, materialized views, and search optimization services.
• Establish secure cross-account data sharing and private connectivity (Snowgrid, AWS PrivateLink).

Preferred Skills

• Advanced Snowflake expertise, including Cortex AI, Snowpark, vector embeddings, and intelligent services (SnowPro Advanced certification preferred).
• Strong experience with Terraform and GitHub Actions for IaC and CI/CD.
• Expert-level SQL skills, query optimization, and governance best practices.
• Experience with hybrid integrations (e.g., SQL Server, Salesforce), real-time streaming, and AI/ML observability.