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Senior Data Engineer ? Fraud Analytics
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Based on Rhode Island data
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What they do
A Data Analyst develops computer programs to analyze large customer information databases for companies and organizations. Analyzes data to identify patterns and provide information relevant to a particular business, industry or field; analysis may be used for marketing, or to detect fraud in financial transactions, or for research. Develops computer programs to protect confidential customer information.
$83,346 / year median in Rhode Island
+6% projected growth
Job Description
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Position range in Rhode Island $80k - $127k Per Year Senior Data Engineer ? Fraud Analytics
(Employer Name Not Available)
Occupation:
Data Scientists
Location:
Smithfield, RI - 02917
Job Type:
Full Time (30 Hours or More)
Posted:
05/27/2026
Positions available: 1
Source:
Intersources Inc.
Web Site:
www.intersourcesinc.com
Job #: 26-00924
Job Requirements and Properties
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Full Time Schedule
Full Time
Job Description
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Title:
Senior Data Engineer -
Fraud Analytics Location:
Merrimack NH /
Smithfield RI On-site/Remote/Hybrid:
Onsite Duration:
6-12
Months Interview Process:
2 Rounds No of submissions:
No of Positions:
Additional Information
Help for Additional Information. Opens a new window. Top Skills
- Design, develop, and optimize data pipelines to support fraud research and analytics use cases
- Research and analyze fraud events by querying and correlating structured and unstructured data across multiple platforms
- Write complex, high-performance SQL queries to extract, transform, and analyze large datasets
- Work extensively with Oracle databases to support enterprise-scale fraud analytics
- Utilize MongoDB for handling semi-structured and unstructured fraud-related data The Role The Senior Data Engineer - Fraud Analytics is responsible for designing, building, and maintaining data solutions that support the research, detection, and analysis of fraud events.
This role partners closely with fraud analysts, investigators, and business stakeholders to transform large, complex datasets into actionable insights that reduce fraud risk and improve detection strategies. Key Responsibilities
- Design, develop, and optimize data pipelines to support fraud research and analytics use cases
- Research and analyze fraud events by querying and correlating structured and unstructured data across multiple platforms
- Write complex, high-performance SQL queries to extract, transform, and analyze large datasets
- Work extensively with Oracle databases to support enterprise-scale fraud analytics
- Utilize MongoDB for handling semi-structured and unstructured fraud-related data
- Investigate data anomalies, identify fraud patterns, and support root-cause analysis
- Partner with fraud operations, compliance, and analytics teams to translate business questions into technical data solutions
- Ensure data accuracy, consistency, and reliability across fraud datasets
- Document data models, logic, and findings clearly for both technical and non-technical audiences
- Communicate findings effectively through written reports, presentations, and stakeholder discussions
- Support continuous improvement of fraud detection and monitoring processes through data-driven insights The Expertise and Skills You Bring Required Skills
- 5+ years of experience in data engineering, analytics, or a related technical role
- Advanced proficiency in SQL, including complex joins, subqueries, performance tuning, and data validation
- Strong hands-on experience with Oracle databases
- Working knowledge of MongoDB or similar NoSQL technologies
- Experience researching fraud events, financial anomalies, risk signals, or suspicious activity (preferred)
- Strong analytical and problem-solving skills with attention to detail
- Excellent written and verbal communication skills, with the ability to explain technical findings to business partners
- Ability to work independently while collaborating across cross-functional teams Preferred Qualifications
- Experience supporting fraud, risk, compliance, or financial crime analytics
- Familiarity with large-scale data environments and data warehousing concepts
- Exposure to scripting or data-processing languages such as Python (nice to have)
- Experience working in regulated or financial services environments
- Strong critical thinking and investigative mindset
- Ability to prioritize and manage multiple research efforts simultaneously
- Comfortable working with ambiguity and incomplete data
- Collaborative, proactive, and detail-oriented.
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