Snowflake Engineer

Snowflake Engineer San Francisco CA - Hybrid Key Skills: Snowflake, Azure, dbt, Kafka Primary Responsibilities Include • Design and implement scalable cloud data warehouse architectures, defining layer structures, schema design patterns, and partitioning/clustering strategies. • Architect physical and logical data models that balance query performance, storage efficiency, and business domain clarity, applying dimensional modeling and data techniques where appropriate. • Adopt cloud data frameworks to build modular, reusable SQL models across staging, intermediate, and mart layers with full documentation and lineage. • Build and maintain data pipelines that ingest data from diverse sources, snowflake shares, databases, APIs, event streams, and flat files into the cloud data warehouse reliably and at scale. • Implement batch and near-real-time ingestion patterns using cloud-native tools, managing incremental loads, CDC (change data capture), and idempotent pipeline design. • Optimize query performance through materialization strategies, clustering keys, and warehouse-specific query tuning techniques. • Implement and maintain role-based access control (RBAC), column-level security, dynamic data masking, and row-level access policies to enforce least-privilege and data privacy requirements. • Establish and maintain CI/CD pipelines for warehouse deployments automating testing, and promotion of transformation code across dev, UAT, and production environments. • Operate within an Agile environment using JIRA to manage work items, participate in sprint planning, and deliver high quality solutions on a consistent cadence. • Apply AI-assisted development tools pragmatically across the engineering lifecycle, accelerating warehouse transformation authoring, data quality automation, and documentation workflows. Qualifications • 10+ years of data engineering experience, with a proven track record of hands on development and end to end solution delivery. • Proven ability to design scalable cloud data warehouse architectures, defining layered structures, schema design patterns and physical data models that balance query performance, storage efficiency, and business domain clarity. • Deep expertise in Snowflake, including data modeling, performance tuning, and cost efficient design, along with experience managing vendor data shares for secure and governed access. • Advanced SQL skills with a strong foundation in data warehousing concepts, including dimensional modeling, incremental processing, slowly changing dimensions, and semantic layers. • Hands-on experience designing and operating cloud data solutions on Azure including ADF, ADLS Storage, with a strong grasp of cloud-native ingestion patterns and pipeline orchestration. • Proficiency in modern data transformation frameworks (dbt) including modular model design across layered warehouse architecture. • Familiarity with streaming and near-real-time ingestion patterns (e.g., Azure Event Hubs, Kafka) including incremental load design, CDC, and latency-aware pipeline considerations. • Strong understanding of platform reliability engineering for data warehouse covering orchestration, backfill and reprocessing strategies, and warehouse performance optimization. • Experience designing and maintaining data quality frameworks with operational alerting and runbooks to support SLA-driven reliability. • Experience implementing CI/CD pipelines for dbt and Snowflake workloads, including Git-based workflows, automated testing, and environment promotions. • Strong written and verbal communication skills with the ability to produce clear data model documentation, pipeline runbooks, and data dictionaries. • Bachelor's degree in computer science, information systems, or a related field. • Experience in asset management, financial services, or investment-related, preferred.