Lead Snowflake Data Engineer (New York, Contractor)

• It is a 100% Onsite position in New York NY
  

• We are seeking a Lead Snowflake Data Engineer to design own and deliver end-to-end data engineering solutions in modern cloud environments.
  
• This role focuses on building scalable high-performance data pipelines using Snowflake and Cortex AI with full lifecycle ownershipfrom ingestion and transformation to modelling optimization and consumption.
  

• Lead the design and development of end-to-end ELT pipelines using Snowflake
  
• Architect scalable data models optimized for performance cost and analytics consumption
  
• Build and maintain backend data services using Python and PySpark
  
• Leverage Snowflake Cortex AI to enable advanced analytics and intelligent data products
  
• Drive performance tuning across pipelines including query optimization clustering and warehouse scaling
  
• Enforce best practices in data governance security and compliance
  
• Collaborate across business analytics and engineering teams to deliver high-quality solutions
  
• Provide technical leadership and mentorship to engineering teams
  
• Communicate architecture decisions and trade-offs effectively in client-facing environments
  

• 10 years of experience or equivalent ownership of production-grade data platforms
  
• Deep expertise in:
  

• Snowflake (data modeling performance tuning optimization)
  
• Python and PySpark
  
• Advanced SQL
  

• Proven ability to design and deliver end-to-end data pipelines (ingestion transformation modeling consumption) in cloud environments (AWS preferred)
  
• Required: Ownership of at least one production-grade Snowflake pipeline end-to-end
  
• Strong foundation in modern data warehousing:
  

• Dimensional modeling (star/snowflake schemas)
  
• ELT/ETL design patterns
  
• Data marts and optimization strategies
  

• Experience with distributed data processing and large-scale datasets
  
• Hands on experience with Snowflake Cortex AI integration
  
• Working knowledge of or similar frameworks
  
• Strong understanding of data governance security and compliance
  
• Ability to:
  

• Clearly explain and defend architectural decisions
  
• Design systems that perform reliably at scale
  
• Balance performance cost and maintainability
  

• Candidates should be able to clearly explain and apply the following in real-world scenarios:
  
• Snowflake Performance & Scaling
  

• Warehouse scaling modes (auto-scale multi-cluster) and when to use them
  
• Clustering keys and performance trade-offs
  
• Cost vs performance optimization strategies
  

• Snowflake Storage & Optimization
  

• Micro-partitioning and its impact on pruning and query performance
  
• Practical optimization techniques for large datasets
  

• End-to-End Pipeline Design
  

• Designing a complete ELT pipeline using Snowflake
  
• Deciding where transformations should occur (Snowflake vs external processing)
  
• Ensuring scalability maintainability and performance across the pipeline Engagement.