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.
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