Data Engineer; Fraud Analytics & Investigative Support

Location: Remote (Occasional Travel May Be Required)

Clearance: Ability to obtain and maintain a Public Trust

U.S. Citizenship is Required.

Position Overview:

Praescient Analytics is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines supporting advanced fraud analytics and investigative solutions for a federal oversight organization. This individual will play a critical role in ensuring diverse data sources are efficiently ingested, transformed, governed, and made available for analytics, machine learning, graph analytics, and investigative support.

The ideal candidate is a hands‑on engineer who enjoys solving complex data integration challenges while building modern cloud‑native data pipelines that prioritize quality, reliability, scalability, and performance. They understand that high‑quality analytics begin with high‑quality data and are committed to developing robust data engineering solutions that enable timely, accurate, and defensible analytic products.

• 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.

• Must have experience with Fraud Analysis
• Three (3) or more years of professional experience in data engineering or a related technical field.
• Demonstrated experience designing, building, maintaining, and optimizing scalable ETL pipelines across diverse data sources.
• 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.

Preference will be given to candidates with demonstrated experience in one or more of the following areas:

• Supporting fraud detection, anomaly detection, financial oversight, program integrity, or investigative analytics environments.
• Building cloud‑native data engineering solutions utilizing Azure Databricks, Azure Data Lake…