The Lead Snowflake Data Engineer will design and deliver data engineering solutions, manage data pipelines, and mentor teams in cloud environments.
PLEASE NOTE:
- It is a 100% Onsite position in New York, NY
JOB DESCRIPTION:
- 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 modelling, 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
Requirements
REQUIRED QUALIFICATIONS:
- 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 HAVE:
- 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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