Snowflake Data Warehouse Engineer

• Design and implement Snowflake schemas and object lifecycle strategies optimized for analytics workloads.
• Build scalable dimensional and time-series data models supporting portfolio hierarchies, positions, security master integration, exposures, and risk metrics.
• Develop robust ELT pipelines using dbt and native Snowflake capabilities, including Streams, Tasks, and Snowpipe, for daily and intraday processing.
• Implement efficient change data capture and incremental processing strategies across position, pricing, risk, and reference data domains.
• Create and maintain high-performance tables, views, and materialized views aligned to performance-sensitive analytical use cases.
• Lead query performance optimization efforts, including clustering strategy, micro-partition awareness, warehouse sizing, caching behavior, and workload management.
• Establish data quality and reconciliation frameworks, including completeness checks, monitoring, and alerting for financial datasets.
• Automate Snowflake deployments and SQL transformations using GitLab CI/CD and version control best practices.

We’re partnering with a confidential, investment-focused organization to add a Snowflake Data Warehouse Engineer. If you have strong Snowflake, SQL, dbt, and performance optimization experience - especially in complex analytical or financial data environments - this is a strong opportunity to work on high-impact data warehouse initiatives., * 7+ years of experience in data engineering or data warehousing roles.

• 5+ years of hands-on Snowflake experience in production environments.
• Experience creating and managing Snowflake databases, schemas, roles, and grants.
• Experience designing and implementing Streams and Tasks for CDC and scheduled processing.
• Experience building and optimizing materialized views and performance-driven data models.
• Expert-level SQL skills, including complex window functions, CTEs, analytic queries, set-based transformations, and performance tuning.
• 4+ years of experience with ELT or transformation tools, ideally dbt.
• Experience integrating Snowflake with Azure cloud storage and upstream financial systems.
• Experience implementing GitLab CI/CD for SQL-based transformations and Snowflake object deployments.
• Strong analytical data modeling experience, including star schemas, slowly changing dimensions, fact tables, aggregates, and large-scale time-series structures.
• Experience supporting high-volume, performance-sensitive financial or portfolio datasets.