Teams that make a difference; individuals that matter in Data Engineering:

At Self Financial, we love to put challenges into the hands of talented individuals to solve. Our teams are composed of skilled professionals, who work together in concert to innovate both our credit building services and the processes we organize around. We empower every individual of the Engineering Organization to bring their creativity and skills to these efforts.  Our goal is to build both world class credit building products and to foster an exciting and engaging work environment.  In short, at Self, you will carry influence.  

As part of the Self Financial Data Engineering Team you will be charged with ensuring that data remains a strategic asset for Self Financial by placing timely, quality managed, and purpose built data into the hands of our team members.  Our team supports internal Self Financial’s Analytics, Machine Learning, and Business Intelligence organizations.

What can you expect to work on/who you will work with?

• The Data Warehouse Engineer is part of the Self Financial Data Engineering Team and operates as part of a cross functional product development team that includes Architecture, Infrastructure, and Product Management
• You'll be primarily focused on modeling and populating data schemas within Self Financial’s Data Environment for use in business intelligence and data analysis activities.
• Your day-to-day activities will include:
• Creating Physical and Logical Data Models with large, complex data sets
• Managing data ETL/ELT processes using SQL and Python
• Maintaining data lifecycle and quality standards
• Creating Data Dictionaries and other relevant documentation
• Working directly with the Product Management to translate Self Financial’s strategic vision into actionable projects

Who you are:

• 3 years of experience with database development, database integration, and data analytics tools
• 3 years of experience designing and implementing complex Data Warehouse and Data Lake data models (Kimball)
• Proficient in managing Data ETL/ELT with large data sets
• Experience with columnar data structures such as Amazon RedShift
• Excellent listening, interpersonal, written, and oral communication skills

What a Successful Candidate Looks like:

• Candidates for the Data Warehouse Engineer position should be able to provide rationale for dimensional modeling and be able to explain how this technique accelerates data analysis.  Candidates are expected to be able to articulate data modeling design decisions and tie those to past job experiences.  

The interview process:

• 30 - 60 minute phone screen with hiring manager
• 1.5 hour coding/design/Q&A session with 2 or more developers
• 30 minute interview with product manager
• 30 minute wrap-up meeting with hiring manager, if moving forward