Data Engineer – Advanced Analytics & Fraud Detection

Description

The Data Engineer will support PRAC’s Advanced Analytic & Investigative Support Services program by designing, building, maintaining, and optimizing data pipelines that support fraud detection, anomaly detection, financial oversight, investigative intelligence, and advanced analytics across large-scale federal benefit programs.

This role will be responsible for ingesting, transforming, validating, cataloging, and preparing data from diverse sources to support data science, graph analytics, forensic accounting, investigative analysis, and reporting efforts. The Data Engineer will work closely with data scientists, graph data scientists, investigative analysts, forensic accountants, and Government stakeholders to ensure that analytic teams have reliable, timely, high-quality data needed to identify fraud, waste, abuse, and mismanagement.

The ideal candidate has hands-on experience with modern data engineering practices, ETL/ELT pipeline development, SQL, Python, cloud-based data platforms, Lakehouse architectures, data quality controls, data lineage, and enterprise data governance.

Key Responsibilities

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### Data Pipeline Development

* Design, build, maintain, and optimize scalable ETL and ELT pipelines supporting advanced analytics and investigative use cases. * Ingest, transform, and prepare data from multiple source formats, including: + Flat files + JSON + XML + Excel files + APIs + Relational databases + Graph databases + Public, non-public, and commercial data sources * Develop repeatable and efficient data workflows that support fraud detection, anomaly detection, graph analysis, risk modeling, and investigative analysis. * Support batch and streaming ingestion frameworks to meet project and operational requirements. * Ensure data pipelines are reliable, efficient, well-documented, and scalable.

### Cloud Data Platform & Lakehouse Support

* Support data engineering activities with