Data Engineer (Fraud Analytics & Investigative Support)

Praescient Analytics; Fairfax (VA); Hybrid; USD 90,000 - 120,000; Full time

Benefits offered by this job

Competitive salary based on qualifications

Comprehensive Company paid healthcare

401(k) with company match

3 weeks paid time off plus Federal Holidays

$5K annual training allowance

Tuition reimbursement program

Job summary

Praescient Analytics is searching for a Data Engineer to develop scalable data pipelines that support advanced fraud analytics initiatives. Located remotely, this role requires the ability to design and maintain robust systems while ensuring data quality and reliability. Ideal candidates should possess strong SQL and Python skills and be skilled in managing cloud-centric data solutions.

Applicants should have experience with data governance and be passionate about solving complex data challenges. Benefits include competitive salary, paid healthcare, and professional development funds.

Qualifications

• Must have experience with Fraud Analysis.
• Three or more years of professional experience in data engineering.
• Experience designing and optimizing scalable ETL pipelines.

Responsibilities

• Design and optimize scalable ETL pipelines for analytics.
• Ingest and transform data from various sources.
• Implement data quality validation and monitoring processes.

Skills

SQL

Python

Data Integration

ETL Pipelines

Data Quality Controls

Tools

Databricks

SQL Server

Azure Data Lake Storage

Apache Spark

Job description

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 engineering solutions utilizing Azure Databricks, Azure Data Lake Storage (ADLS), Microsoft SQL Server, Microsoft Fabric, Azure Synapse Analytics, Power BI, Neo4j, Git repositories, or comparable cloud data platforms.
• Developing scalable data pipelines supporting machine learning, artificial intelligence (AI), graph analytics, natural language processing (NLP), or advanced analytics solutions.
• Working with public, non‑public, commercial, financial, law enforcement, or cross‑agency datasets supporting fraud detection and investigative missions.
• Designing and implementing Lakehouse architectures, Delta Lake, data partitioning strategies, and performance optimization techniques for large‑scale analytics environments.
• 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.
• Utilizing orchestration and workflow tools such as Apache Spark, Databricks Workflows, Azure Data Factory, Airflow, or comparable pipeline automation technologies.
• Collaborating within Agile software development teams using Git‑based version control, sprint planning, backlog management, and continuous integration/continuous deployment (CI/CD) practices.
• Supporting Offices of Inspector General (OIGs), federal oversight organizations, law enforcement agencies, or other government data modernization initiatives.

What We're Looking For

We're looking for 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, solving integration challenges, and continuously improving the quality, performance, and accessibility of enterprise data. They understand that trustworthy analytics depend on trustworthy data and take pride in developing robust engineering solutions that enable investigators and analysts to uncover fraud, waste, abuse, and emerging risks with confidence.

What you can expect from us

• Real opportunity for career growth in an environment where your achievements will be celebrated
• Constant collaboration with numerous teams to ensure client success
• A team that respects and embraces your ideas and expertise
• Coworkers that are motivated by pursuing excellence, rather than the prospect of personal gain
• A workplace dedicated to supporting and bettering public safety and government agencies

Benefits

• Competitive salary based on qualifications and experience
• Comprehensive, Company paid healthcare for you (We pay your premiums and deductibles)
• 401(k) with company match
• Travel & performance incentives
• 3 weeks paid time off (plus Federal Holidays)
• $5K annual training allowance
• $500 book allowance
• Tuition reimbursement program

Praescient Analytics is an Equal Employment Opportunity employer. Employment decisions are based on merit, qualifications, experience, performance, business needs, and applicable contract requirements. Praescient does not unlawfully discriminate or provide disparate treatment based on race, ethnicity, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other status protected by applicable law. Praescient Analytics acknowledges the applicable clause and provision updates implementing Executive Order 14398, Addressing DEI Discrimination by Federal Contractors, and the related FAR/RFO updates, including FAR 52.222‑90 where applicable. Praescient does not engage in racially discriminatory DEI activities, including disparate treatment based on race or ethnicity in recruitment, hiring, promotion, contracting, program participation, training, mentoring, leadership development, or allocation of company resources. Praescient’s employment and contracting decisions are made based on merit, qualifications, experience, performance, business needs, and applicable contract requirements.

Applicants selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information. US Citizenship Required. Interested Candidates: Please forward your resume to [e-mail] and please visit our website to apply online at www.praescientanalytics.applicantstack.com/x/openings.