Staff Data Engineer

SoFi is seeking an experienced and motivated Staff Data Engineer to be the technical leader of our Data Engineering group within the Data Enablement division. The mission of the Data Enablement division is to activate data throughout SoFi, enabling the creation of personalized and delightful experiences for our members. 

As a technical leader, you will help lead the vision and strategy to build foundational and critical data products which are highly leveraged across SoFi for off-line analytical, reporting, and machine learning use-cases as well as more critical online use cases. Our goal is to empower all teams at SoFi to make data driven decisions and effectively measure their results by providing high quality, high availability data, and democratized data access through self-service tools. 

Role: 

• A talented, enthusiastic, and detail-oriented experienced Data Engineer who knows how to take on big data challenges in an agile way. This includes big data design and analysis, data modeling, and development, deployment, and operations of big data pipelines. 
• Leads development of some of the most critical data pipelines and datasets and expands self-service data knowledge and capabilities.
• Build scalable foundational data models that can be highly leveraged across SoFi for analytical, reporting, and machine learning.
• This role requires you to live at the cross section of data and engineering. 
• You have a deep understanding of data, analytical techniques, and how to connect insights to the business, and you have practical experience in insisting on highest standards on operations in ETL and big data pipelines. 

What you’ll do 

• Provide technical leadership and strategic guidance to the data engineering team. 
• Design and develop robust data architectures and data pipelines to support data ingestion, processing, storage, and retrieval. 
• Evaluate and select appropriate technologies, frameworks, and tools to build scalable and reliable data infrastructure. 
• Optimize data engineering systems and processes to handle large-scale data sets efficiently. 
• Design solutions that can scale horizontally and vertically. 
• Collaborate with cross-functional teams, such as data scientists, software engineers, and business stakeholders, to understand data requirements, influence best practices upstream and down, and deliver solutions that meet business needs. 
• Effectively communicate complex technical concepts and trade offs to non-technical stakeholders and senior management verbally and in well written technical documents. 
• Optimize data engineering systems and processes to handle large-scale data sets efficiently. 
• Enforce data governance policies and practices to maintain data integrity, security, and compliance with relevant regulations. 
• Collaborate with data governance and security teams to implement robust data protection mechanisms and access controls. 
• Provide mentorship and guidance to the data engineering team, fostering a culture of continuous learning, innovation, and excellence. 
• Contribute to hiring and training efforts to build a skilled and motivated data engineering workforce.
• Be part of an on call support rotation to support the EDW

What You'll Need: 

• A bachelor’s degree in computer science, Data Science, Engineering, or a related field. 
• Over 8 years of experience in data engineering and analytics, with a proven track record of successfully building data teams. 
• Proficiency in data engineering tech stack; Snowflake / Python / SQL / GitLab / AWS / Airflow. 
• Proficiency in relational database platforms and cloud database platforms such as Snowflake, Redshift, or GCP
• Strong in Python and/or another data centric language.  
• Thorough knowledge and passion around data modeling, database design, data architecture principles, and data operations. 
• Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans. 
• Experience in a highly regulated and governed sector, though the Fintech industry is advantageous.