Snowflake Data Engineer

Key responsibilities
Design, build, test, deploy, and maintain scalable data pipelines, transformations, and data models on Snowflake. Develop and productionize AI and machine learning solutions using Snowflake Cortex AI, including AI agents, retrieval and search experiences, and external-facing applications. Create, validate, version, and maintain semantic views and related business logic to support accurate, governed AI and analytics experiences. Build and support ingestion patterns using Snowflake Openflow, Kafka, Snowpipe, APIs, and third-party connectors, including integrations with enterprise applications such as NetSuite. Develop reusable engineering components and automation using advanced SQL, SnowSQL or Snowflake CLI, Python, and, where appropriate, JavaScript. Implement data quality checks, automated testing, observability, error handling, and deployment controls to ensure reliable production data and AI workloads. Optimize data models, queries, pipelines, and AI workloads for performance, maintainability, scalability, and efficient consumption. Design and implement secure Snowflake data sharing and data-product patterns for internal teams, partners, and customers. Establish software engineering practices for Snowflake development, including source control, CI/CD, environment promotion, documentation, and release management. Troubleshoot pipeline, application, semantic-layer, and data-quality issues and drive them through resolution. Partner with the Snowflake Account Administrator and security teams on required roles, privileges, SSO/SCIM integration, governance policies, and production readiness without assuming ownership of account administration. Collaborate with data engineering, analytics, application, product, and business teams to translate high-value use cases into durable data and AI solutions.

Required experience
Prior hands-on experience as a Snowflake data engineer, AI engineer, analytics engineer, or similar role delivering production solutions on Snowflake. Strong Snowflake SQL skills and demonstrated experience developing data pipelines, transformations, data models, and performance-tuned workloads. Experience building and maintaining applications or AI/ML solutions with Snowflake Cortex AI or comparable large language model and machine learning technologies. Experience designing semantic layers or semantic views that translate business concepts into governed, reusable definitions for analytics and AI. Proficiency in Python and experience applying software engineering practices such as testing, source control, CI/CD, and code review. Experience with streaming, event-driven, or connector-based ingestion using technologies such as Kafka, Openflow, Snowpipe, APIs, or comparable integration frameworks. Experience supporting production data platforms on Azure and integrating with enterprise identity and security patterns. Ability to diagnose and resolve complex data, pipeline, application, and performance issues. Ability to work cross-functionally with platform administration, security, data engineering, analytics, application, and business teams.

Preferred qualifications
Experience creating, deploying, evaluating, and maintaining AI agents or generative AI applications in production. Experience with Snowflake Cortex Analyst, Cortex Search, Snowpark, Streamlit in Snowflake, or Snowflake-native application development. Experience with Snowflake data sharing, Marketplace listings, secure data products, or customer-facing data delivery patterns. Experience implementing Openflow or Kafka-based ingestion and integrating SaaS or ERP sources such as NetSuite. Experience with Azure services, Azure AD SSO/SCIM integration, and secure cloud application patterns. Experience working with SQL Server in a hybrid SQL Server and Snowflake environment. JavaScript development experience is a plus. Snowflake certifications are a plus. Experience supporting regulated, payment, financial-services, or other security-sensitive environments is strongly preferred.

What success looks like
Reliable, scalable data pipelines and data products move from development to production with strong quality and observability. AI agents and applications deliver accurate, governed, measurable value to internal users and external customers. Semantic views remain trusted, testable, and resilient as underlying data and business definitions evolve. New data sources and streaming workloads are integrated efficiently using maintainable, reusable engineering patterns. Data and AI workloads are performant, cost-aware, documented, and supported through disciplined software delivery practices. Engineering teams can move quickly while account administration, security, and governance responsibilities remain clearly separated and well coordinated.