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 ownership—from ingestion and transformation to modeling, optimization, and consumption.

Key Responsibilities

• 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

Required Qualifications & Technical Expertise

• 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 React.js 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

Technical Depth (Must Be Demonstrated)

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.