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, machine learning, and investigative workflows for a federal oversight organization.

Responsibilities

• Design, build, maintain, and optimize scalable ETL pipelines that enable advanced analytics and investigative workloads.
• Ingest, transform, and integrate structured and unstructured data from diverse sources such as flat files, JSON, XML, Excel, APIs, graph databases, relational databases, and evolving formats.
• Develop and optimize data pipelines for both streaming and batch ingestion frameworks.
• Manage and organize data within cloud-based analytics platforms, including Databricks Unity Catalog, SQL Server managed instances, and Lakehouse architectures.
• Develop SQL and Python based data transformations to support downstream analytics, machine learning, graph analytics, and business intelligence solutions.
• Implement data quality validation, lineage tracking, metadata management, and monitoring 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 EDM 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.
• 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.

Technologies

• SQL
• Python
• Databricks Unity Catalog
• SQL Server
• Databricks
• Azure Databricks
• Azure Data Lake Storage (ADLS)
• 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 (Occasional Travel May Be 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 support advanced fraud analytics and investigative solutions for a federal oversight organization. This role ensures 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 collaborates with data scientists, investigators, and project managers to support analytic initiatives.

What We're Looking For

We are seeking a data engineer who is passionate about building reliable, scalable data foundations that power advanced analytics. The ideal candidate enjoys working with complex data ecosystems, addressing integration challenges, and continually improving data quality, performance, and accessibility. They understand that trustworthy analytics depend on trustworthy data and will contribute robust engineering solutions that enable investigators and analysts to uncover insights.

What You Can Expect From Us

• Real opportunity for career growth in an environment where achievements are recognized
• Constant collaboration with multiple teams to ensure client success
• A team that respects and embraces your ideas and expertise
• Coworkers motivated by excellence rather than personal gain
• A workplace dedicated to supporting and strengthening public safety and government agencies

Preferred Qualifications

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