Data Engineer (Fraud Analytics & Investigative Support) with Security Clearance

Key Responsibilities

• Design, develop, maintain, and optimize scalable ETL pipelines supporting advanced analytics and investigative workloads.
• Ingest, transform, and integrate structured and unstructured data from diverse sources including flat files, JSON, XML, Excel, APIs, graph databases, relational databases, and other evolving data formats.
• Develop and optimize data pipelines supporting both streaming and batch ingestion frameworks.
• Manage, organize, and optimize data within modern cloud-based analytics platforms, including Databricks Unity Catalog, SQL Server managed instances, and Lakehouse architectures.
• Develop efficient SQL and Python-based data transformation processes that support downstream analytics, machine learning, graph analytics, and business intelligence solutions.
• Implement data quality validation, lineage tracking, metadata management, and monitoring processes to ensure data reliability and integrity throughout the analytics lifecycle.
• Collaborate with Data Scientists, Graph Data Scientists, Investigative Analysts, Forensic Accountants, and Project Managers to understand data requirements and support analytic initiatives.
• Troubleshoot pipeline failures, optimize performance, and continuously improve scalability, reliability, and maintainability of enterprise data solutions.
• Support enterprise data governance by implementing data management standards, documenting data assets, and ensuring compliance with enterprise data management (EDM) policies.
• Contribute to data architecture improvements, ingestion strategies, and modernization efforts that enhance overall analytic capabilities.

Required Qualifications

• Must have experience with Fraud Analysis
• Three (3) or more years of professional experience in data engineering or a related technical field.
• Strong SQL and Python programming skills, or equivalent technologies, for data ingestion, transformation, and processing.
• Experience ingesting and transforming data from flat files, JSON, XML, Excel, APIs, graph databases, relational databases, and other structured and unstructured data sources.
• Experience loading, managing, and optimizing data within Databricks Unity Catalog, SQL Server managed instances, or comparable cloud-based data platforms.
• Experience working with streaming and batch ingestion frameworks and modern Lakehouse architectures.
• Demonstrated ability to implement data quality controls, lineage tracking, reliability monitoring, and performance optimization processes.
• Familiarity with enterprise data governance, enterprise data management (EDM), metadata management, and data quality best practices.
• Strong analytical, problem-solving, written, and verbal communication skills.