
Snowflake Solutions Engineer
Job Description
Job Description
- Design and develop interactive data applications using Snowflake Streamlit for self-service analytics and operational workflows that enable business users to interact with data through intuitive interfaces
- Create reusable application frameworks and component libraries for rapid solution delivery
- Integrate Snowflake Native Apps and third-party marketplace applications to extend platform capabilities
- Develop custom UDFs and stored procedures to support advanced application logic and business rules
- Design and implement modern data architecture solutions spanning data warehousing, data lakes, and lakehouse patterns
- Implement and maintain medallion architecture (bronze-silver-gold) patterns for data quality and governance
- Evaluate and recommend architecture patterns for diverse use cases including structured analytics, semi-structured data processing, and AI/ML workloads
- Establish best practices for data organization, storage optimization, and query performance across different data architecture patterns
- Support AI and data science teams with Snowflake platform capabilities and best practices
- Collaborate on implementing Snowflake Cortex AI features for business use cases
- Provide technical guidance on data access patterns and feature engineering for AI workloads
- Design data structures and access patterns optimized for ML model training and inference
- Participate in proof-of-concepts for AI capabilities and provide platform expertise
- Design and implement role-based access control (RBAC) hierarchies following least privilege principles
- Establish security best practices including network policies, authentication methods, data encryption, and row or column level security and masking.
- Implement object tagging strategies and tag-based policies for access control and governance
- Monitor and optimize application performance, query efficiency, and user experience
- Establish cost optimization strategies for compute resources and storage across different workload patterns
- Provide technical guidance on Snowflake capabilities, features, and roadmap to stakeholders
- Lead architectural discussions on solution design patterns and technology selection
- Create technical documentation, implementation guides, and best practice recommendations
- Bachelor's degree in Computer Science, Information Systems, Data Engineering, Data Science or related technical field
- At least 2 years of recent hands-on experience with Snowflake platform including advanced features
- Minimum 3 years of experience in data engineering or solutions architecture roles
- 7-10 years of experience in Data Architecture/Engineering and/or BI in a multi-dimensional environment
- Proven track record of developing data applications or analytical solutions for business users
- Snowflake Expertise: Advanced knowledge of Snowflake architecture including data warehousing, data lakes, and emerging lakehouse features
- Security and Governance: Deep understanding of RBAC, row-level security, data masking, and Snowflake security best practices
- DevOps and CI/CD: Strong experience with GitHub, SnowDDL, automated deployment pipelines, and infrastructure as code
- Application Development: Proficiency with Snowflake Streamlit for building interactive data applications
- SQL Proficiency: Expert-level SQL skills with experience in complex analytical queries and optimization
- Python Programming: Strong Python skills for Snowpark development, data processing, and application logic
- Data Architecture: Deep understanding of data warehousing concepts, data lake patterns, and modern lakehouse architectures
- Backup and Recovery: Experience with disaster recovery planning, backup automation, and data retention strategies
- Certifications: Snowflake SnowPro Core, Advanced Architect, or Data Engineer certification
- AI/ML Collaboration: Experience supporting data science teams and understanding ML workflow requirements
- Development Frameworks: Experience with modern web frameworks, API development, and microservices
- Cloud Platforms: Knowledge of AWS, Azure, or Google Cloud data services and integration patterns
- Data Governance: Understanding of data cataloging, metadata management, and governance frameworks
- DevOps Tools: Experience with GitHub Actions, Jenkins, GitLab CI/CD, or similar automation platforms
- Infrastructure as Code: Proficiency with SnowDDL, Terraform, Schemachange, or other IaC tools for Snowflake