ZipRecruiter

Log In

1

Software Engineer Fraud Detection Jobs in Washington

Magnus Management Group LLC

Data Engineer

Washington, DC · Remote

$117K - $140K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Must have experience supporting fraud detection, anomaly detection, or financial oversight analytics environment preferred. Minimum Qualifications * Minimum 3 years data engineering experience.

GDIT

Software Engineer 3

Annapolis Junction, MD

$175K - $238K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... detection Experience with Git Source Control System Position Desired Skills Familiar with HPC Job ... We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding ...

Serco USA

Sr. Manager Fraud Prevention

Herndon, VA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... fraud detection and risk operations for a large federal contract at our Headquarters office in ... Manage a software driven information collection and analysis system used to track fraud and provide ...

previous page

Showing results 41-60

All JobsSoftware Engineer Fraud Detection JobsSoftware Engineer Fraud Detection Jobs in Washington

Software Engineer Fraud Detection information

What does a software engineer fraud detection do?

A Software Engineer in Fraud Detection designs and develops systems to identify and prevent fraudulent activities within digital platforms, such as banking or e-commerce environments. They build algorithms to analyze user behavior, detect anomalies, and flag suspicious transactions in real time. Their work often involves machine learning, big data analysis, and close collaboration with data scientists and security teams to continuously improve fraud detection accuracy. These engineers play a key role in protecting businesses and customers from financial loss and cybercrime.

How does a software engineer fraud detection typically collaborate with data scientists and analysts to identify fraudulent activity?

Software Engineers in Fraud Detection work closely with data scientists and analysts to build, refine, and deploy systems that detect and prevent fraud. While data scientists may develop models and identify patterns from large datasets, engineers are responsible for integrating these models into scalable, real-time systems within the company's technology stack. Regular communication and joint problem-solving are essential, as engineers must understand the logic behind models and analysts' findings to ensure accurate implementation and continuous improvement. This collaborative environment helps create robust fraud detection mechanisms that adapt to evolving threats.

What are the key skills and qualifications needed to thrive as a software engineer fraud detection, and why are they important?

To thrive as a Software Engineer in Fraud Detection, strong programming skills (such as Python, Java, or Scala), a solid understanding of algorithms, data structures, and experience with machine learning or statistical analysis are generally required, often supported by a degree in computer science or a related field. Familiarity with big data platforms (like Hadoop or Spark), real-time analytics systems, and fraud detection tools or frameworks is typically expected. Analytical thinking, problem-solving abilities, and effective communication are key soft skills that differentiate top performers in this field. These skills are crucial for developing robust systems that can quickly identify and prevent fraudulent activities, protecting both users and organizations.

What is the difference between Software Engineer Fraud Detection vs Data Scientist Fraud Detection?

AspectSoftware Engineer Fraud DetectionData Scientist Fraud Detection
Required CredentialsBachelor's in CS or related field, programming skillsBachelor's or higher in CS, Statistics, or Data Science
Work EnvironmentDevelops fraud detection systems, writes code, implements algorithmsAnalyzes data, builds models, interprets results
Employer & Industry UsageFinancial institutions, fintech, e-commerceFinancial services, tech companies, insurance
Common Search & ComparisonFocuses on software development for fraud detectionFocuses on data analysis and modeling for fraud detection

While both roles work in fraud detection, Software Engineer Fraud Detection primarily develops and maintains detection systems through coding, whereas Data Scientist Fraud Detection analyzes data and builds models to identify fraudulent activity. Both roles often collaborate but differ in their core focus and skill sets.

What are popular job titles related to Software Engineer Fraud Detection jobs in Washington?

For Software Engineer Fraud Detection jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Software Engineer Fraud Detection jobs in Washington look for?

The top searched job categories for Software Engineer Fraud Detection jobs in Washington are:

What cities in Washington are hiring for Software Engineer Fraud Detection jobs?

Cities in Washington with the most Software Engineer Fraud Detection job openings:

Software Engineer Fraud Detection jobs near you

Data Engineer

Magnus Management Group LLC

Washington, DC • Remote

$117K - $140K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 4 days ago

Job description

Benefits:
  • 401(k)
  • Dental insurance
  • Health insurance
  • Paid time off
  • Vision insurance

Seeking a Data Engineer responsible for designing, developing, and maintaining scalable enterprise data pipelines supporting advanced fraud analytics. The candidate will manage ingestion, transformation, quality control, and optimization of structured and unstructured data across cloud-based analytics platforms.
Responsibilities
  • Should bring a minimum of three (3) years of professional experience in data engineering or a related field. 
  • Demonstrate the ability to design, build, and maintain scalable ETL pipelines across diverse data sources. 
  • Should apply strong SQL and Python skills, or equivalent technologies, to ingest and transform data from flat files, JSON, XML, Excel, APIs, graph databases, with flexibility to adapt to additional formats and sources as needed. 
  • Should possess experience loading, managing, and optimizing data within platforms such as Databricks Unity Catalog and SQL Server managed instances, including work with streaming and batch ingestion frameworks and modern Lakehouse architecture. 
  • Should exhibit strong capabilities in implementing standard quality control processes to ensure data quality, lineage, reliability, and performance while collaborating effectively with cross‑functional teams. 
  • Must have familiarity with data governance, data quality, and data management practices consistent with enterprise data management (EDM) standards. 
  • Must have experience supporting fraud detection, anomaly detection, or financial oversight analytics environment preferred. 
Minimum Qualifications
  • Minimum 3 years data engineering experience. 
  • Strong SQL skills. 
  • Strong Python programming. 
  • Experience building ETL pipelines. 
  • Experience with Databricks. 
  • Experience with Azure SQL or SQL Server. 
  • Experience implementing data quality processes. 
Preferred Qualifications
  • Experience with fraud detection analytics. 
  • Experience with streaming data pipelines. 
  • Experience supporting enterprise data governance. 

This is a remote position.