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.

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 dat...