Job description

Employee Applicant Privacy Notice Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. The Role: SoFi's Fraud Prevention Center of Excellence (CoE) is responsible for managing enterprise fraud threats across banking products and member-related activities. The Fraud Operations Analytics & Reporting team sits at the center of this mission — driving intelligence through data to improve operational efficiency, agent accountability, and fraud detection efficacy across all fraud product lines. We are looking for a Senior Data Engineer to build and scale the data infrastructure that powers the Fraud CoE. This role is the foundational engineering resource for the Fraud Operations Analytics team, responsible for consolidating data pipelines into governed, source-of-truth data marts and enabling the analytical work that drives operational decisions at the highest levels of the organization. Key Responsibilities Data Infrastructure & Pipeline Development Design, build, and maintain scalable data pipelines that consolidate fraud operational data from multiple source systems into governed Snowflake data marts Partner with Fraud Data Engineering and the TDM platform team on the foundational Fraud CoE data buildout, including summarized metric tables that standardize reporting across all fraud product lines Data Modeling & Warehouse Optimization Model analytical data within Snowflake to support fraud operations reporting, agent performance scorecards, and fraud tagging quality monitoring Build and maintain dbt models that structure data for easy consumption by analysts and Tableau reporting layers Ensure query performance, data freshness, and reliability across all fraud data mart tables powering production dashboards and leadership reports Analytics Enablement Partner closely with fraud analysts to understand data requirements and translate them into robust, reusable data structures Develop and maintain summarized metric tables that lock reporting logic centrally, ensuring consistency across reporting Governance & Standards Establish and enforce data engineering standards, documentation practices, and pipeline design patterns for the Fraud CoE Ensure all data assets are properly documented, tested, and governed to support audit and compliance requirements Serve as the data engineering subject matter expert in cross-functional discussions with Fraud Strategy, Fraud Platform, Risk Infrastructure, and Data Engineering partners What We're Looking For Required 4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent) Strong data modeling skills — experience designing analytical data structures optimized for reporting and self-service consumption Ability to work closely with analytics and business stakeholders to translate requirements into scalable data solutions Strong documentation habits and commitment to data quality, testing, and pipeline reliability Exposure to dbt Cloud, Airflow/MWAA, or similar orchestration platforms Strongly Preferred Experience in financial services, fraud, risk, or compliance data environments Familiarity with fraud operations data concepts — fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue management Compensation and Benefits The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location. To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page! SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law. The Company hires the best qualified candidate for the job, without regard to protected characteristics. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. New York applicants: Notice of Employee Rights SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com. We are unable to accommodate remote work from Hawaii, Alaska or Puerto Rico at this time. Internal Employees If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

Requirements

Must-haves

Employee Applicant Privacy Notice
Data Infrastructure & Pipeline Development
Design, build, and maintain scalable data pipelines that consolidate fraud operational data from multiple source systems into governed Snowflake data marts
Partner with Fraud Data Engineering and the TDM platform team on the foundational Fraud CoE data buildout, including summarized metric tables that standardize reporting across all fraud product lines
Data Modeling & Warehouse Optimization
Model analytical data within Snowflake to support fraud operations reporting, agent performance scorecards, and fraud tagging quality monitoring
Build and maintain dbt models that structure data for easy consumption by analysts and Tableau reporting layers
Ensure query performance, data freshness, and reliability across all fraud data mart tables powering production dashboards and leadership reports
Analytics Enablement
Partner closely with fraud analysts to understand data requirements and translate them into robust, reusable data structures
Develop and maintain summarized metric tables that lock reporting logic centrally, ensuring consistency across reporting
Governance & Standards
Establish and enforce data engineering standards, documentation practices, and pipeline design patterns for the Fraud CoE
Ensure all data assets are properly documented, tested, and governed to support audit and compliance requirements
Serve as the data engineering subject matter expert in cross-functional discussions with Fraud Strategy, Fraud Platform, Risk Infrastructure, and Data Engineering partners
4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms
Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent)
Strong data modeling skills — experience designing analytical data structures optimized for reporting and self-service consumption
Ability to work closely with analytics and business stakeholders to translate requirements into scalable data solutions
Strong documentation habits and commitment to data quality, testing, and pipeline reliability
Exposure to dbt Cloud, Airflow/MWAA, or similar orchestration platforms
Experience in financial services, fraud, risk, or compliance data environments
Familiarity with fraud operations data concepts — fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue management

Nice-to-haves

Strongly Preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpython

Platform: greenhouse
Effort: quick