Lead Snowflake Engineer
1 week ago
Key Responsibilities:
- Lead the end-to-end Snowflake platform implementation, including architecture, design, data modeling, and governance.
- Oversee the migration of data and pipelines from legacy platforms to Snowflake, ensuring quality, reliability, and business continuity.
- Design and optimize Snowflake-specific data models, including use of clustering keys, materialized views, Streams, and Tasks.
- Build and manage scalable ELT/ETL pipelines using modern tools and best practices.
- Define and implement standards for Snowflake development, testing, and deployment, including CI/CD automation.
- Collaborate with cross-functional teams including data engineering, analytics, DevOps, and business stakeholders.
- Establish and enforce data security, privacy, and governance policies using Snowflake's native capabilities.
- Monitor and tune system performance and cost efficiency through appropriate warehouse sizing and usage patterns.
- Lead code reviews, technical mentoring, and documentation for Snowflake-related processes.
- Directs technical quality efforts, including code reviews and performance tuning, to ensure the system meets high standards before deployment.
- Leads the technical planning for deployment, oversees the cutover process, and ensures the system is stable, monitored, and performant in the production environment.
Required Snowflake Expertise:
- Snowflake Architecture – Deep understanding of virtual warehouses, data sharing, multi-cluster, zero-copy cloning.
- Performance Optimization – Proficient in tuning queries, clustering, caching, and workload management.
- Data Engineering – Experience with Snowpipe, Streams & Tasks, stored procedures (JavaScript-based), and data ingestion patterns.
- Data Security & Governance – Strong experience with RBAC, dynamic data masking, row-level security, and tagging.
- Advanced SQL – Expertise in complex SQL queries, transformations, semi-structured data handling (JSON, XML).
- Cloud Integration – Integration with major cloud platforms (AWS/GCP/Azure) and services like S3, Lambda, Step Functions, etc.
- Experience with ETL orchestration tools such as Airflow, DBT, and Matillion.
- Proficiency in handling semi-structured data formats, including JSON and Parquet.
- Familiarity with Git-based version control systems, including branching, merging, and pull request workflows.
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