Snowflake Architect

Snowflake Architect

Must Have Technical/Functional Skills

• Strong hands-on experience with Snowflake architecture and performance tuning
• Expertise in DBT (models, testing, macros, documentation, environments)
• Solid experience with ETL/ELT frameworks and data integration patterns
• Proficiency in Python for data engineering and automation
• Experience with Snowpark Implementation
• Strong knowledge of cloud data services (AWS, Azure, or GCP)
• Advanced SQL and data modeling skills

Roles & Responsibilities

We are seeking an experienced Snowflake Data Architect to design, build, and optimize scalable cloud-based data platforms. The ideal candidate will have deep expertise in Snowflake, DBT, Snowpark, ETL/ELT pipelines, Python, and cloud data services (AWS, Azure, or GCP). This role will lead architecture decisions, ensure best practices, and enable analytics and data science teams with high-quality, reliable data solutions.

Architecture & Design

• Design and implement end-to-end Snowflake-based data architectures for analytics, reporting, and advanced data use cases
• Define data modeling strategies (dimensional, data vault, and analytical models) optimized for Snowflake
• Establish standards for data ingestion, transformation, storage, and consumption.

Snowflake Platform Management

• Architect and manage Snowflake features including Warehouses, Databases, Schemas, Cloning, Time Travel, Secure Data Sharing, Data Clean Rooms and Resource Monitoring
• Optimize performance and cost using warehouse sizing, clustering, caching, and query optimization
• Implement security best practices including RBAC, masking policies, row access policies, and data governance.

Data Transformation & ETL/ELT

• Lead ELT pipeline development using DBT (models, macros, tests, documentation, and deployments)
• Design and implement ETL/ELT pipelines using cloud-native Snowpark and third-party tools.
• Implement Real time streaming and Batch data Processing.
• Ensure data quality, lineage, and observability across pipelines.

Cloud & Big Data Integration

• Architect solutions leveraging cloud data services (AWS, Azure, or GCP) such as object storage, messaging, and orchestration services
• Integrate Apache Spark (Databricks or equivalent) for large-scale data processing and advanced transformations
• Support hybrid and multi-cloud data architectures.

Development & Automation

• Develop data processing and automation solutions using Python
• Build reusable frameworks for ingestion, transformation, validation, and monitoring
• Implement CI/CD pipelines for data workloads and DBT, Snowpark deployments.