Lead Snowflake Data EngineerLocation: NYC | Fully Onsite | Prioritizing local candidates Contract with potential for extensionWe 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 ResponsibilitiesLead the design and development of end-to-end ELT pipelines using SnowflakeArchitect scalable data models optimized for performance, cost, and analytics consumptionBuild and maintain backend data services using Python and PySparkLeverage Snowflake Cortex AI to enable advanced analytics and intelligent data productsDrive performance tuning across pipelines, including query optimization, clustering, and warehouse scalingEnforce best practices in data governance, security, and complianceCollaborate across business, analytics, and engineering teams to deliver high-quality solutionsProvide technical leadership and mentorship to engineering teamsCommunicate architecture decisions and trade-offs effectively in client-facing environmentsRequired Qualifications & Technical Expertise10+ years of experience, or equivalent ownership of production-grade data platformsDeep expertise in:Snowflake (data modeling, performance tuning, optimization)Python and PySparkAdvanced SQLProven 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-endStrong foundation in modern data warehousing:Dimensional modeling (star/snowflake schemas)ELT/ETL design patternsData marts and optimization strategiesExperience with distributed data processing and large-scale datasetsHands on experience with Snowflake Cortex AI integrationWorking knowledge of React.js or similar frameworksStrong understanding of data governance, security, and complianceAbility to:Clearly explain and defend architectural decisionsDesign systems that perform reliably at scaleBalance performance, cost, and maintainabilityTechnical Depth (Must Be Demonstrated)Candidates should be able to clearly explain and apply the following in real-world scenarios:Snowflake Performance & ScalingWarehouse scaling modes (auto-scale, multi-cluster) and when to use themClustering keys and performance trade-offsCost vs performance optimization strategiesSnowflake Storage & OptimizationMicro-partitioning and its impact on pruning and query performancePractical optimization techniques for large datasetsEnd-to-End Pipeline DesignDesigning a complete ELT pipeline using SnowflakeDeciding where transformations should occur (Snowflake vs external processing)Ensuring scalability, maintainability, and performance across the pipelineEngagementVendor-facing role requiring strong communication and stakeholder alignmentAbility to operate independently within complex enterprise environmentsFocus on delivering production-grade, scalable data solutions