Data Engineer – Advanced Analytics & Fraud Detection

The Leading Niche is seeking a Data Engineer to support their Advanced Analytic & Investigative Support Services program. The role involves designing, building, and maintaining data pipelines to support fraud detection and advanced analytics across large-scale federal benefit programs.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• 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
• 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

Requirements:

• Minimum three (3) years of professional experience in data engineering or a related field
• Experience designing, building, and maintaining scalable ETL pipelines across diverse data sources
• Strong SQL skills
• Strong Python skills or equivalent programming experience
• Experience ingesting and transforming data from: Flat files, JSON, XML, Excel, APIs, Graph databases
• Experience working with Databricks Unity Catalog
• Experience working with SQL Server Managed Instances
• Experience with streaming ingestion frameworks
• Experience with batch ingestion frameworks
• Experience working within modern Lakehouse architectures
• Experience implementing data quality processes
• Experience implementing data lineage practices
• Experience ensuring data reliability and performance
• Experience collaborating with cross-functional technical and mission teams
• Familiarity with enterprise data governance, data quality, and enterprise data management standards
• Experience supporting fraud detection, anomaly detection, financial oversight analytics, or investigative analytics environments
• Experience supporting PRAC, CIGIE, Offices of Inspector General, federal law enforcement, or federal oversight organizations
• Experience with large-scale federal benefit programs such as PPP, EIDL, RRF, SVOG, unemployment insurance, or similar programs
• Familiarity with Azure Databricks, Microsoft SQL Server, Microsoft Power BI, Neo4j, and related analytic tools
• Experience supporting entity resolution, graph analytics, machine learning, or investigative intelligence workflows
• Experience working with public, non-public, and commercially available datasets
• Knowledge of data privacy, data sensitivity classification, metadata management, and data-sharing requirements
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Data Analytics, Mathematics, Engineering, or a related field preferred