We are looking for a Snowflake Data Engineer (8-10 years of experience) to architect, build, and scale our data platform. In this role, you won't just be writing queries; you will be responsible for the full-stack Snowflake ecosystem, including performance tuning, cost management, and leveraging the latest features like Snowpark and Cortex AI.

Key Responsibilities :

- Warehouse Architecture : Design and implement highly performant Snowflake environments, including multi-cluster warehouses, resource monitors, and custom scaling policies.

- Modern Data Pipelines : Build end-to-end ELT pipelines using Snowpipe, Streams, and Tasks for real-time and batch ingestion.

- Snowpark Development : Use Snowpark (Python/Java) to migrate complex transformation logic from legacy systems directly into Snowflake's elastic compute.

- Optimization & Tuning : Conduct deep-dive analysis into query profiles to optimize micro-partitioning, clustering keys, and materialized views.

- Governance & Security : Implement robust security frameworks using Row-Level Security (RLS), Object Tagging, and Dynamic Data Masking.

- Cost Management : Monitor credit consumption and implement strategies to ensure maximum ROI on Snowflake spend.

Technical Skills & Qualifications :

Experience : 8 to 10 years in Data Engineering, with at least 4+ years of dedicated Snowflake development and at least 2+ years of dedicated python development.

Core Expertise :

- Advanced SQL : Expertise in analytical functions, CTEs, and UDFs (User Defined Functions).

- Data Modeling : Proficiency in Star Schema, Snowflake-specific optimizations (clustering)

- Python/Snowpark : Strong ability to write Python code for data processing within the Snowflake environment.

- Ecosystem Knowledge : Experience with dbt (data build tool) for modular SQL modeling and Git for version-controlled data pipelines.

- Cloud Infrastructure : Familiarity with cloud storage (S3, Azure Blob, or GCS) and how Snowflake interacts with external stages.