Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
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