Azure Security Engineer

• Implement Snowflake schemas and object lifecycle strategies optimized for risk and portfolio analytics workloads.
• Develop scalable, dimensional, and time-series data models supporting portfolio hierarchies, positions, security master integration, exposures, and risk metrics.
• Build robust ELT pipelines using dbt and native Snowflake capabilities, including Streams, Tasks, and Snowpipe, to support daily and intraday data processing.
• Implement efficient change data capture and incremental processing strategies for position, pricing, risk, and reference data.
• Develop and maintain high-performance tables, views, and materialized views aligned with performance-sensitive analytical use cases.
• Lead query performance optimization efforts, including clustering strategy design, micro-partition awareness, warehouse sizing, caching behavior, and workload management.
• Establish data quality frameworks, including reconciliation controls, completeness checks, monitoring, and alerting for financial datasets.
• Automate Snowflake deployments and SQL transformations using GitLab CI/CD pipelines and version control best practices.
• Document data models, lineage, architectural decisions, governance controls, and operational runbooks.
• Troubleshoot production failures, perform root cause analysis, and remediate issues impacting reporting timelines.

A senior-level Snowflake Data Warehouse Engineer with 7+ years of data engineering experience and 5+ years of hands-on Snowflake experience in production environments. This role will deliver scalable, high-performant data warehouse powering investment risk and portfolio analytics. The ideal candidate has built and optimized enterprise-grade analytical platforms, implemented robust CDC frameworks, and supported performance-sensitive financial reporting workloads. The resource will assist with developing reliable, governed, and performant data solutions that enable exposure reporting, factor analysis, time-series risk metrics, stress testing, and performance attribution. This role works closely with business intelligence engineers, data architects, and data scientists., * 7+ years of experience in data engineering or data warehousing roles.

• 5+ years of hands-on Snowflake experience in production environments, including:
• Creating and managing databases, schemas, roles, and grants.
• Designing and implementing Streams and Tasks for CDC and scheduled processing.
• Building and optimizing materialized views and performance-driven data models.
• Expert-level SQL skills, with deep experience in complex window functions, CTEs, analytic queries, set-based transformations, and query performance tuning.
• 4+ years of experience with ELT frameworks or transformation tools, preferably dbt.
• Experience integrating Snowflake with Azure cloud storage and upstream financial systems.
• Demonstrated experience implementing GitLab CI/CD for SQL-based transformations and Snowflake object deployments.
• Strong understanding of analytical data modeling, 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.
• Familiarity with portfolio accounting, security master data, market data feeds, or risk concepts such as VaR, factor exposure, stress testing, or performance attribution.