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.