Snowflake Architect

Job DescriptionSnowflake ArchitectMust 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 skillsRoles & ResponsibilitiesWe 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.________________________________________Key Responsibilities:Architecture & DesignDesign and implement end-to-end Snowflake-based data architectures for analytics, reporting, and advanced data use casesDefine data modeling strategies (dimensional, data vault, and analytical models) optimized for SnowflakeEstablish standards for data ingestion, transformation, storage, and consumption.Snowflake Platform ManagementArchitect and manage Snowflake features including Warehouses, Databases, Schemas, Cloning, Time Travel, Secure Data Sharing, Data Clean Rooms and Resource MonitoringOptimize performance and cost using warehouse sizing, clustering, caching, and query optimizationImplement security best practices including RBAC, masking policies, row access policies, and data governance.Data Transformation & ETL/ELTLead 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 IntegrationArchitect solutions leveraging cloud data services (AWS, Azure, or GCP) such as object storage, messaging, and orchestration servicesIntegrate Apache Spark (Databricks or equivalent) for large-scale data processing and advanced transformationsSupport hybrid and multi-cloud data architectures.Development & AutomationDevelop data processing and automation solutions using PythonBuild reusable frameworks for ingestion, transformation, validation, and monitoringImplement CI/CD pipelines for data workloads and DBT, Snowpark deployments.