Snowflake Lead Developer – Data Modeling & Data Engineering
Job Title: Snowflake Lead Developer Data Modeling & Data Engineering
Location:
Milpitas CAONSITE ROLELooking for an experienced Snowflake Developer with strong expertise in advanced SQL and Data Modeling, capable of independently designing and developing logical, physical, and dimensional data models. Hands-on experience with Snowflake, DBT/ETL-ELT, and Python is required. Exposure to AI/GenAI capabilities such as Snowflake Cortex AI, RAG, embeddings, vector search, and AI-ready data pipelines is highly preferred. 6+ years of overall Data Engineering/Data Warehousing experience, including strong hands-on Snowflake experience. Role Descriptions
Experience: 10+ YearsJob Summary
We are seeking an experienced Snowflake Developer with strong expertise in SQL| Data Modeling| and Data Warehousing. The ideal candidate will be responsible for designing and developing scalable data models| building data pipelines| optimizing Snowflake performance| and enabling enterprise reporting and analytics. The candidate should have hands-on experience in creating logical| physical| and dimensional data models and translating business requirements into efficient data solutions.
Key Responsibilities:
Design| develop| and maintain logical| physical| and dimensional data models in Snowflake.
Build and optimize scalable data warehouse solutions to support enterprise reporting and analytics.
Develop complex SQL queries| stored procedures| views| and data transformation logic.
Create and maintain star schema| snowflake schema| and dimensional models.
Collaborate with business stakeholders| data architects| and analytics teams to understand data requirements.
Develop and support ETL/ELT pipelines for structured and semi-structured data.
Perform data profiling| data quality validation| and source-to-target mapping.
Optimize Snowflake performance through partitioning| clustering| query tuning| and warehouse sizing.
Implement data governance| security| and best practices for data management.
Support production issues| performance bottlenecks| and data reconciliation activities.
Create technical documentation including data dictionaries| mapping documents| and model designs.