Lead Snowflake Engineer

7 days ago

Ahmedabad, Gujarat, India Anblicks Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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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