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

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 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.
* 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.

Preferred Qualifications:

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…