Data Engineer (Fraud Analytics & Investigative Support)

Praescient Analytics seeks a Data Engineer to design, build, and maintain scalable cloud-native data pipelines that support fraud analytics, graph analytics, ML, and investigative workflows for a federal oversight organization.

Responsibilities

• Design, develop, maintain, and optimize scalable ETL pipelines for 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 evolving formats.
• Develop and optimize pipelines for both streaming and batch ingestion.
• Manage 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 transformations to support downstream analytics, machine learning, graph analytics, and business intelligence.
• Implement data quality validation, lineage tracking, metadata management, and monitoring to ensure data reliability across 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 policies.
• Contribute to data architecture improvements, ingestion strategies, and modernization efforts that enhance analytic capabilities.

Requirements

• Experience with fraud analysis is required.
• Three or more years of professional experience in data engineering or a related technical field.
• Proven ability to design, build, maintain, and optimize scalable ETL pipelines across diverse data sources.
• Strong SQL and Python programming skills 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 sources.
• Experience loading 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.
• Ability to implement data quality controls, lineage tracking, reliability monitoring, and performance optimization.
• Familiarity with enterprise data governance, EDM, metadata management, and data quality best practices.
• Strong analytical, problem-solving, and written and verbal communication skills.

Technologies

• SQL
• Python
• Databricks Unity Catalog
• SQL Server
• Databricks
• Azure Databricks
• Azure Data Lake Storage
• Microsoft Fabric
• Azure Synapse Analytics
• Power BI
• Neo4j
• Git
• Databricks Workflows
• Azure Data Factory
• Airflow
• Apache Spark
• Delta Lake

Benefits

• Competitive salary based on qualifications and experience
• Comprehensive, company paid healthcare for you
• 401(k) with company match
• Travel and performance incentives
• Three weeks paid time off plus Federal Holidays
• $5,000 annual training allowance
• $500 book allowance
• Tuition reimbursement program

Location

Remote work is available with occasional travel required.

Clearance

• Ability to obtain and maintain a Public Trust
• U.S. Citizenship is required

Position Overview

Praescient Analytics seeks an experienced Data Engineer to design, build, and maintain scalable data pipelines that enable advanced fraud analytics and investigative solutions for a federal oversight organization. This role ensures diverse data sources are efficiently ingested, transformed, governed, and made accessible for analytics, machine learning, graph analytics, and investigative support. The ideal candidate is hands-on and excels at solving complex data engineering challenges.

What We're Looking For

The ideal candidate focuses on building reliable, scalable data foundations that empower advanced analytics. They enjoy navigating complex data ecosystems, addressing integration challenges, and continuously improving data quality, performance, and accessibility to support investigators and analysts.

What You Can Expect From Us

Opportunities for career growth within an environment that recognizes achievements. Collaborative, team oriented culture that values ideas, with colleagues driven by excellence and public safety and government mission success.

Preferred Qualifications

• Experience supporting fraud detection, anomaly detection, financial oversight, program integrity, or investigative analytics.
• Developing cloud native data engineering solutions using Azure Databricks, ADLS, Microsoft SQL Server, Microsoft Fabric, Azure Synapse Analytics, Power BI, Neo4j, Git, Databricks Workflows, Azure Data Factory, or Airflow.
• Building scalable data pipelines for machine learning, AI, graph analytics, NLP, or advanced analytics.
• Experience with public, non public, commercial, financial, law enforcement, or cross-agency datasets used for fraud detection and investigations.
• Designing Lakehouse architectures, Delta Lake, data partitioning, and performance optimization for large-scale analytics.
• Automating data quality validation, metadata management, lineage tracking, schema evolution, and monitoring.
• Supporting enterprise data governance initiatives, data catalogs, master data management, and adherence to data standards.
• Using orchestration and workflow tools such as Spark, Databricks Workflows, Azure Data Factory, Airflow, or similar tooling.
• Collaborating in Agile teams using Git, sprint planning, backlog management, and CI/CD practices.
• Experience supporting Offices of Inspector General and federal oversight organizations along with government data modernization initiatives.