Senior Data Engineer

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., 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

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