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 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.