Staff Data Engineer

Requirements 8+ years of experience with database development, database integration, and data analytics tools 8+ years of experience designing and implementing complex Data Warehouse and Data Lake data models (Kimball) Willingness to embrace the responsibilities of team leadership and accountability for team results Proficient in managing Data ETL/ELT with large data sets Experience with columnar data structures such as Amazon RedShift Familiarity with AWS data warehousing tools Experience with common software engineering tools such as Git, JIRA, Confluence and similar platforms Familiarity with Apache Airflow and Python Excellent listening, interpersonal, written, and oral communication skills What the job involves As our Staff Data Engineer, you will be responsible for organizing plans that drive outcomes and making a direct impact on our mission. We want you to provide technical leadership to your teammates through coaching and mentorship, and collaborate cross-functionally to implement impactful improvements to our product As part of the Self Financial Data Engineering Team, your role will be to ensure that data remains a strategic asset for Self Financial by delivering timely, high-quality, and purpose-built data to our team members. Our team supports Self Financial's internal Analytics, Machine Learning, and Business Intelligence organizations Operates as part of a cross functional product development team that includes Architecture, Infrastructure, and Product Management Your primary focus will be on leading the data pipeline, 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: Collaborating closely with Product Management to translate Self Financial's strategic vision into actionable projects Architecture design, project analysis, work break down and planning Creating Physical and Logical Data Models with large, complex data sets Managing data pipeline, ETL/ELT processes using SQL and Python Maintaining data lifecycle and quality standards #J-18808-Ljbffr