Investment Data Architect

Position Summary: 

Securian Financial is seeking an experienced and coaching-focused Investment Data Platform Architect to guide the project team in its implementation of an Investment Data Store.  On the cutting edge of Securian’s data strategy, this role plays an active part in implementing technology patterns that will form the foundation of value-driven data processing.  The right candidate will have a proven history of implementing data engineering patterns, possess a keen eye for data governance considerations and display a passion for collaborating and coaching within a team.   

Qualifications: 

• 5+ years professional experience in data engineering or related discipline. 
• Demonstrated expertise utilizing the Snowflake platform, including experience using Marketplace and Snowpark. 
• Demonstrated expertise utilizing AWS services in the creation of data pipelines. 
• Knowledgeable of multiple data engineering implementation patterns across third party tools and homegrown solutions. 
• Passion and proven history of sharing expertise in data space to coach project team members and ensure they have the know-how to lead future build-out. 
• Knowledgeable of various Data Governance considerations and how to build them into data workflows. 
• Experience with data visualization tools (Tableau) is preferred. 
• Experience with Institutional Investment data is highly favorable. 
• Experience with Agile methodologies and practices is preferred. 
• Strong history of documenting work in ways that efficiently facilitate knowledge transfer. 
• Excellent teamwork and interpersonal skills, with experience working both independently, and in a team-oriented, collaborative environment. 

Responsibilities include but not limited to: 

• Partner with project team members to advise on the architectural, accessibility, and storage setup of an Investment Data Store. 
• Partner with project team members to advise, design and implement data engineering patterns and processes. 
• Partner with project team members to experiment with various integration methods and data analysis tools and techniques. 
• Influence designs to incorporate elements of data quality checks, data validation and error handling, and data governance considerations/requirements. 
• Coach project team engineers to help them build expertise in data workflows and enable them to take lead on implementation activities. 
• Analyze historical data stores and advise on strategies to migrate data while ensuring integrity of data. 
• Collaborate with team members to peer review deliverables and maintain team quality/standards. 
• Document technical processes and decisions to assist with maintenance activities and ongoing knowledge sharing