Sr Snowflake/DWH/ETL Informatica Consultant

Role Description

The Contractor requires the following skills, abilities, and background:

• 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.

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.

The selected Contractor will be responsible for the following essential activities:

• 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.

Qualifications

• 7+ years of experience in data engineering or data warehousing roles.
• 5+ years of hands-on Snowflake experience in production environments.
• Expert-level SQL skills.
• 4+ years of experience with ELT frameworks or transformation tools, preferably dbt.
• Experience integrating Snowflake with Azure cloud storage.
• Demonstrated experience implementing GitLab CI/CD.
• Strong understanding of analytical data modeling.
• Experience supporting high-volume financial datasets.
• Familiarity with portfolio accounting and risk concepts.

Requirements

• Implement Snowflake schemas and object lifecycle strategies.
• Develop scalable data models.
• Build robust ELT pipelines.
• Implement change data capture strategies.
• Develop and maintain high-performance tables and views.
• Lead query performance optimization efforts.
• Establish data quality frameworks.
• Automate Snowflake deployments.
• Document data models and operational runbooks.
• Troubleshoot production failures.

Company Description