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
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 within modern Lakehouse architectures.
- Load, manage, and optimize data within platforms such as Databricks Unity Catalog and SQL Server Managed Instances.
- Support data cataloging, schema management, table creation, transformation logic, and data access workflows.
- Maintain and enhance existing data pipelines, catalogs, schemas, and transformation logic.
- Ensure continuity of existing operational data services and prevent disruption to production analytics.
Data Quality, Lineage & Reliability
- Implement data quality checks, validation rules, reconciliation processes, and reliability controls.
- Identify and resolve data anomalies, missing values, duplicate records, formatting issues, and transformation errors.
- Support data lineage practices to ensure transparency from source ingestion through final analytic output.
- Document data movement, transformation rules, source mappings, assumptions, and quality control procedures.
- Monitor pipeline performance and implement improvements to enhance reliability, efficiency, and scalability.
Support for Fraud Analytics & Investigative Workflows
- Prepare datasets used for fraud detection rules, machine learning models, graph analytics, entity resolution, and investigative analysis.
- Collaborate with data scientists and graph data scientists to structure data for model development, link analysis, and network analysis.
- Support financial oversight and investigative teams by ensuring data is accessible, accurate, and fit for analytic use.
- Assist with integrating multi-agency and multi-program datasets to identify complex fraud patterns and cross-program risks.
- Support analytics involving federal benefit programs, suspicious entities, transactions, applications, relationships, and fraud indicators.
Data Governance & Enterprise Data Management
- Follow enterprise data management standards and Government data governance requirements.
- Support data privacy, sensitivity categorization, metadata management, and data dictionary development.
- Ensure data engineering practices align with applicable governance, security, privacy, and compliance standards.
- Collaborate with Government stakeholders and technical teams to maintain enterprise data inventories and data-sharing documentation.
- Support implementation of data governance, data quality, and data management best practices.
Cross-Functional Collaboration
- Work closely with data scientists, investigative analysts, forensic accountants, graph data scientists, project managers, and Government stakeholders.
- Participate in sprint planning, technical discussions, project meetings, and integrated project team sessions.
- Translate analytic and investigative requirements into practical data engineering solutions.
- Support troubleshooting and issue resolution across data pipelines, databases, and analytic platforms.
- Communicate technical findings, risks, dependencies, and recommendations clearly to both technical and non-technical audiences.
Documentation & Deliverables
- Document data sources, ingestion processes, transformation logic, schemas, data quality rules, and pipeline workflows.
- Maintain technical documentation, project artifacts, and code repositories.
- Support project deliverables, monthly progress updates, data inventories, and analytic work products.
- Ensure data engineering outputs are accurate, repeatable, auditable, and aligned with project requirements.