Staff Data Engineer

About the role

Self Financial is a venture-backed, high-growth FinTech company with a mission to increase economic inclusion and financial resilience by empowering people to build credit and build savings. We are looking for people who share our passion and are driven to tackle challenges, find solutions and make the financial space better for the communities we serve. Our team is passionate about challenging the status quo of the credit industry by providing people accessible tools to take control of their credit. Executing on our mission requires deep collaboration across our teams to ensure our products reach the people who can benefit from them the most, particularly the 100 million+ Americans who have no or low credit. We celebrate diversity and are committed to creating an inclusive environment for all employees. To that end, we seek to recruit, develop and retain the most talented people from a diverse candidate pool. You will operate 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. You will provide technical leadership to your teammates through coaching and mentorship, and collaborate cross-functionally to implement impactful improvements to our product.

Key facts

What you'll do

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.

Collaborates closely with Product Management to translate Self Financial's strategic vision into actionable projects.

Architects and design robust data architecture solutions that align with product requirements and long term business objectives.

Analyzes complex business problems and converts them into logical and physical data models that support scalable reporting and analytics.

Manages robust data pipeline, ETL/ELT processes using SQL and Python to ensure timely and reliable data movement.

Establishes and maintains data lifecycle and quality standards to ensure accuracy, consistency, and reliability of datasets.

Implements monitoring and observability practices to detect data issues early and maintain high confidence in analytics outputs.

Participates in on-call rotations to support data infrastructure in production and respond to urgent business needs.

Drives data governance initiatives to ensure compliance, security, and best practices across the data platform.

Works closely with Data Scientists and Business Intelligence teams to optimize data structures for analytical and machine learning workloads.

Identifies opportunities for process automation and performance improvements within the data ecosystem.

Contributes to technical documentation and knowledge sharing to enable team scalability and effective collaboration.

Mentors data engineers and provides technical guidance to help the team grow their skills and deliver high quality solutions.

Evaluates new tools, frameworks, and technologies to determine their applicability and benefit to the data organization.

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.

Practical notes

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