Principal Solutions Architect - Data Engineering

Role details

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. PRINCIPAL SOLUTIONS ARCHITECT We are seeking experienced professionals with deep expertise in Data Engineering, Cloud Data Architecture, and AI-assisted, agentic development practices to join our Services Delivery team to help create exciting new offerings and capabilities for our customers! In this strategic role, you will help customers modernize and expand their use of the Snowflake AI Data Cloud. You will take data pipelines from ideation to full production and pioneer agentic data engineering, where AI agents write, test, deploy, monitor, and optimize data workloads alongside engineers. You will use Snowflake's native capabilities across ingestion, transformation, orchestration, and governance, along with our partner ecosystem, to advise clients on best practices for resilient, cost-efficient, production-ready data architectures. You will design tailored data engineering solutions and define repeatable agent-driven delivery patterns that speed up migrations and platform builds. You will also work closely with customer teams and Systems Integrators, providing the technical leadership and oversight needed for successful outcomes. AS A PRINCIPAL SOLUTIONS ARCHITECT AT SNOWFLAKE, YOU WILL: - Be a technical expert on all aspects of Snowflake as it applies to data engineering to provide customers with best practices given Snowflake's technology stack. - Work with customers to understand their data engineering and platform modernization goals, discover key requirements, and architect a Snowflake-centric solution to be delivered by Services Delivery. - Understand how to build and deploy data pipelines with Snowflake features within the Snowflake ecosystem based on customer requirements. - Pioneer agentic data engineering practices. Use AI coding agents and Snowflake's AI capabilities to speed up pipeline development, code conversion and migration, testing, documentation, and day-to-day operations. Turn these approaches into repeatable delivery patterns and accelerators. - Work hands-on where needed in SQL, Python, Snowpark, dbt, and agentic development tools to build POCs and reference implementations that show engineering techniques and best practices on Snowflake. - Follow best practices, including ensuring knowledge transfer so that customers are properly enabled and are able to extend the capabilities of Snowflake on their own. - Maintain a deep understanding of competing and complementary technologies and vendors in data engineering and in AI-assisted development, and know how to position Snowflake against them. - Guide customers through their specific technical challenges, such as complex migrations, performance tuning, and cost optimization. - Support other members of the Services Delivery team as they develop their expertise. - Work with Product Management, Engineering, and Marketing to keep improving Snowflake's products and marketing. Feed real-world delivery insights back into the roadmap for data engineering and agentic features. OUR IDEAL SOLUTION ARCHITECT - DATA ENGINEERING WILL HAVE: - Minimum 5 years experience working with customers in a pre-sales or post-sales technical role. - Outstanding skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos. - Thorough understanding of the complete data engineering lifecycle, including data ingestion, transformation, orchestration, data modeling, and data quality. - Thorough understanding of modern data architecture patterns, including batch and streaming pipelines, ELT, lakehouse and open table formats (Apache Iceberg), and data warehouse migration and modernization. - Strong understanding of DataOps, along with the technologies and methods for deploying, testing, monitoring, and governing data pipelines (CI/CD, observability, performance tuning, and cost optimization). - Hands-on experience applying AI coding agents and agentic workflows to accelerate data engineering work such as pipeline development, code conversion, testing, and operations. - Experience and understanding of at least one public cloud platform (AWS, Azure or GCP). - Experience with at least one data platform or integration technology such as Databricks, AWS Glue/EMR, Azure Data Factory/Synapse, GCP BigQuery/Dataflow, Informatica, Fivetran, etc. - Hands-on scripting experience with SQL and at least one of the following: Python, Java or Scala. - Experience with tools and frameworks such as dbt, Apache Spark/PySpark, Apache Airflow, Kafka, Pandas, or similar. - University degree in computer science, engineering, data science, mathematics or related fields, or equivalent experience. BONUS POINTS FOR HAVING: - Experience migrating Databricks/Apache Spark or legacy ETL workloads (e.g., Informatica, SSIS, DataStage) to Snowflake - Experience building agentic workflows, custom agent skills, or MCP-based tooling to automate data engineering tasks - Experience with AI/ML workloads, including preparing data for feature engineering, RAG, or agent use cases, and understanding of ML/DL fundamentals. - Snowflake certifications (e.g., SnowPro Advanced: Data Engineer or Architect) - Proven success within enterprise software - Vertical expertise in a core vertical such as FSI, Retail, Manufacturing etc. Every Snowflake