Senior Staff Data Engineer (R13274)

ABOUT OPORTUN

Oportun (Nasdaq: OPRT) is a mission-driven fintech that puts its members' financial goals within reach. With intelligent borrowing, savings, and budgeting capabilities, Oportun empowers members with the confidence to build a better financial future. Since inception, Oportun has provided more than $19.7 billion in responsible and affordable credit, saved its members more than $2.4 billion in interest and fees, and helped its members save an average of more than $1,800 annually. Oportun has been certified as a Community Development Financial Institution (CDFI) since 2009.

 

WORKING AT OPORTUN

Working at Oportun means enjoying a differentiated experience of being part of a team that fosters a diverse, equitable and inclusive culture where we all feel a sense of belonging and are encouraged to share our perspectives. This inclusive culture is directly connected to our organization's performance and ability to fulfill our mission of delivering affordable credit to those left out of the financial mainstream. We celebrate and nurture our inclusive culture through our employee resource groups.

Position Overview:

The Staff Software Engineer role offers a unique opportunity to combine your technical expertise with strategic thinking, guiding the team in making architectural decisions, mentoring senior engineers, and actively contributing to the evolution of our technology stack.  This role is the SME and “go-to” person in the team, and the individual works well under pressure, responds with urgency and operates with a high degree of autonomy, accountability and maturity. As a Staff Software engineer, you are the domain expert in multiple areas, and can provide regular insight to product and design leads of all levels that are working within these areas. You can identify, tackle and deliver on strategically important problems for the group whether it is Product, Platform, or Infrastructure. This role affords opportunities to lead and contribute to design and implementation of multiple large projects, or lead a large org or company-wide objective, including the possibility of (technically) leading multiple small(er) teams, or a large team. Overall, this role has all the ingredients for the incumbent to command and hold respect as an expert and to develop a track-record of deep and/or broad impact to a product or technology area.  

RESPONSIBILITIES

1. Technical Leadership

Provide technical leadership and mentorship to the data engineering team, promoting best practices, code quality, and professional growth. Act as a subject matter expert in data engineering, staying updated with industry trends and advancements, and sharing knowledge with the team.   2. Architectural Design and Development: Lead the design and implementation of complex data architectures, ensuring scalability, reliability, and performance to meet business objectives and analytical needs. Collaborate with stakeholders while demonstrating strong business context to define data architecture standards and guidelines.   3. Data Pipeline Development and Optimization: Design and develop efficient and scalable data pipelines, ensuring data ingestion, processing, and transformation align with business requirements. Optimize data pipelines for performance, resilience, and cost-effectiveness.   4. Database Management and Optimization: Oversee the design and management of databases, data warehouses, and data lakes, optimizing for performance, security, and scalability. Implement and optimize ETL processes to maintain data integrity and efficiency in data storage solutions.   5. Technical Ownership and Execution: Lead and manage complex data engineering projects, ensuring successful project delivery within specified timelines and budget. Collaborate with cross-functional teams to align project goals and priorities, providing technical expertise to meet project requirements.   6. Data Quality and Governance: Establish and enforce data quality standards, governance policies, and compliance with relevant regulations, ensuring data accuracy, consistency, and security. Implement data quality monitoring and improvement processes.   7. Collaboration and Stakeholder Management: Collaborate with various stakeholders to understand their data needs and provide data solutions that drive business value. Communicate effectively wit