Senior Data Engineer

SoFi•Cottonwood Heights, UT

About The Position

SoFi’s Fraud Prevention Center of Excellence manages enterprise fraud threats across banking products and member-related activities. The Fraud Operations Analytics & Reporting team uses data to improve operational efficiency, agent accountability, and fraud detection efficacy across fraud product lines. The Senior Data Engineer will build and scale the data infrastructure powering the Fraud CoE. As the foundational engineering resource for the Fraud Operations Analytics team, this role will consolidate data pipelines into governed, source-of-truth data marts and enable analytics that inform operational decisions. The position works with Fraud Data Engineering, the TDM platform team, fraud analysts, and cross-functional partners to develop dependable data pipelines, Snowflake models, and standardized reporting metrics.

Requirements

At least 4 years of data engineering experience.
Hands-on expertise in SQL, Python, and cloud data warehouse platforms.
Experience building and maintaining production-grade data pipelines using modern data stack tools such as dbt, Airflow, or equivalent.
Strong data modeling skills, including designing analytical data structures optimized for reporting and self-service consumption.
Ability to work 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.

Nice To Haves

Experience in financial services, fraud, risk, or compliance data environments.
Familiarity with fraud operations data concepts such as fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue management.

Responsibilities

Design, build, and maintain scalable 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 fraud product lines.
Model analytical data in Snowflake for fraud operations reporting, agent performance scorecards, and fraud tagging quality monitoring.
Build and maintain dbt models for analysts and Tableau reporting layers.
Ensure query performance, data freshness, and reliability across fraud data mart tables used by production dashboards and leadership reports.
Work with fraud analysts to understand data requirements and translate them into robust, reusable data structures.
Develop and maintain summarized metric tables that centralize reporting logic and ensure consistency across reporting.
Establish and enforce data engineering standards, documentation practices, and pipeline design patterns for the Fraud CoE.
Ensure data assets are 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.