Solutions Architect
The Solutions Architect advises enterprise customers, designs Snowflake data and AI architectures, and leads implementation of analytics, generative AI, and MLOps solutions. The role requires technical consulting experience, strong SQL and Python skills, and expertise across modern data platforms and the AI/ML lifecycle.
About the job
Responsibilities
Customer Engagement & Advisory
- Engage directly with customers as a trusted advisor for technical thought leadership and solution approaches.
- Extract business requirements and translate them into well-architected recommendations.
- Develop forward-looking guidance and future-state roadmaps.
- Apply best practices and solutions to reduce implementation risk.
- Collaborate with go-to-market and product teams to align customer solutions with Snowflake goals.
Technical Leadership & Architecture
- Oversee and advise on implementation of customers’ Snowflake platforms.
- Demonstrate technical leadership through code, solution architectures, and implementation recommendations.
AI/ML Solutions & MLOps
- Design, prototype, and implement scalable, performant end-to-end AI/ML solutions on Snowflake.
- Advise customers on best practices for AI/ML workloads in Snowflake.
- Define and advise on MLOps practices for model deployment, monitoring, and governance.
Product Adoption
- Drive adoption of Snowflake’s AI product suite, including Cortex, Streamlit in Snowflake, and Snowflake Intelligence.
- Replatform AI/ML workloads to Snowflake based on business and operational requirements.
- Coach customers on leveraging Snowflake’s AI product suite to create business value.
Requirements
- Bachelor’s degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience.
- 3 years of technical consulting experience.
- Experience leading analytics projects for large enterprises.
- Experience implementing and operating Snowflake-centric solutions.
- Understanding of the complete data analytics stack, from ETL and data platform design through BI and analytics tools.
- Deep understanding of the AI/ML lifecycle, including data preparation, feature engineering, model training, deployment, and monitoring.
- Familiarity with AI/ML and transformation technologies.
- Hands-on experience with MLOps tools and frameworks for deploying, managing, and governing machine-learning models at scale.
- Experience developing generative AI and LLM use cases.
- Familiarity with common BI and ETL solutions.
- Hands-on technical experience using SQL and Python; Java or Spark is optional.
- Extensive knowledge of large-scale database technologies such as Snowflake, Netezza, Exadata, Teradata, and Greenplum.
- Proficiency implementing data security measures, access controls, and security designs within Snowflake.
- Familiarity with prompt engineering techniques.
Nice-to-Haves
- At least 5 years of experience as a solutions architect, data architect, database administrator, or data engineer.
- Hands-on experience building and managing AI/ML workloads.
- Experience in a product company’s services organization.
- Experience in a leadership position executing technical projects.
- Application development experience.
- AWS, Google Cloud, or Microsoft cloud certification.
- Snowflake SnowPro Advanced certification.
Compensation & Benefits
- Salary and benefits information is provided on the Snowflake Careers Site for jobs located in the United States.
Skills
Snowflake, SQL, Python, Machine Learning, MLOps, Generative AI, LLMs, Prompt Engineering, ETL, Business Intelligence, Data Architecture, Data Security, AWS, GCP, Spark
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