The Senior Data Engineer supports government-led fraud analytics initiatives by designing, building, and maintaining scalable data pipelines, integration frameworks, and analytical data environments. This role is responsible for enabling predictive modeling, fraud detection, and operational analytics through high-quality, reliable, and secure data architecture.

The position plays a critical role in supporting model development, monitoring, and production operations by ensuring seamless data ingestion, transformation, and availability across structured and unstructured data sources. The Senior Data Engineer works closely with data scientists, analysts, and government stakeholders to operationalize fraud detection capabilities and enhance analytical performance across filing and non-filing periods.

Key Responsibilities

• Design, develop, and maintain robust ETL/ELT pipelines supporting fraud analytics and modeling efforts
• Integrate data from internal IRS systems and external data sources to support fraud detection and taxpayer protection strategies
• Support development and deployment of predictive models, filters, and business rules in production and non-production environments
• Ensure data quality, integrity, and consistency across all analytical platforms
• Optimize data pipelines for performance, scalability, and reliability
• Support data migration and consolidation efforts across legacy and modernized environments
• Enable real-time and batch data processing for fraud monitoring and response activities
• Collaborate with data scientists and analysts to structure datasets for modeling and reporting
• Implement data governance, metadata management, and documentation standards
• Provide technical support for production systems and troubleshoot data-related issues
• Support reporting and dashboard development by ensuring availability of clean, structured datasets

Supported PWS Tasks

• 4.5 Model Development
• 4.6 Monitoring
• 4.7 Data Migration
• 4.13 Production Support
• 4.15 Reporting Support

Day-to-Day Activities

• Develop and maintain ETL pipelines using tools such as Python, SQL, or cloud-native data services
• Ingest and transform large datasets from multiple structured and unstructured sources
• Monitor pipeline performance and resolve data processing issues
• Collaborate with analytics teams to prepare datasets for fraud models and rule engines
• Perform data validation, cleansing, and reconciliation activities
• Support deployment and maintenance of data workflows in production environments
• Document data flows, schemas, and transformation logic
• Participate in daily standups, sprint planning, and technical reviews
• Respond to ad hoc data requests supporting emerging fraud schemes or analytical needs
• Assist in implementing enhancements to improve data processing efficiency and accuracy