Job description

The Fraud Data Engineering team, within the Fraud & Global Security Division, delivers reliable, scalable, and secure data solutions that power fraud detection, prevention, analytics, and decision-making across Navy Federal Credit Union. Our mission is to enable data-driven fraud strategies by providing high-quality data, modern data platforms, and engineering solutions that support Fraud Operations, Fraud Strategy & Reporting, Fraud Detection, Fraud Data Science and Machine Learning models & Fraud Investigation teams. The Summer Associate will provide analytical and technical support to the Fraud Data Engineering team by assisting with data integration, data quality, analytics, and platform modernization initiatives. The intern will gain hands-on experience working with large-scale fraud datasets, cloud technologies, and modern data engineering tools used to support critical fraud prevention and detection activities. Potential Summer Associate projects may include: • Assist in data readiness for AI use by supporting transformation, data quality, and accessibility efforts that enable AI and machine learning solutions. • Collaborate with Data Engineers, Data Scientists, Fraud Analysts, and business stakeholders on data-driven projects. • Collaborate with cross-functional teams to understand business challenges and translate them into data-driven solutions. The Summer Associate Program is a 12-week internship program beginning in May 2027 and ending in August 2027 Students will work on impactful projects and meaningful work during their internship. To qualify for this position, applicants must be currently pursuing a degree from an accredited college or university and have an anticipated graduation date of December 2027 or later. • Collaborate with engineers, analysts, and business partners to gather requirements and deliver solutions. • Assist in creating business requirement documents, build STTMs, testing, and validating the fraud and security-related data. • Enable real-time and batch data integration across enterprise systems and external fraud data providers. • Support fraud business teams with timely, accurate, and actionable data solutions. • Analyze data to identify quality issues, trends, and opportunities for improving operational efficiency. • Document technical processes, data flows, and engineering solutions. • Participate in Agile team activities, including planning sessions, standups, and project reviews. • Contribute to the development of Databricks Notebooks, enhance UAT testing & data validation framework. • Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, Software Engineering, Analytics, or a related field. • Experience with programming languages such as Python, SQL, Scala, Java, or similar technologies. • Familiarity with relational databases, data warehousing concepts, and data modeling. • Basic understanding of ETL/ELT processes and data integration concepts. • Strong analytical and problem-solving skills with the ability to work with large and complex datasets. • Knowledge of cloud platforms (Azure, AWS, or GCP) is preferred. • Familiarity with big data technologies such as Databricks, Spark, Kafka, or Hadoop is a plus. • Strong communication, collaboration, and technical presentation skills. Additional Information Hours: • Monday – Friday, 8:00 AM – 4:30 PM Location : • 820 Follin Lane SE, Vienna, VA 22180 The Fraud Data Engineering team, within the Fraud & Global Security Division, delivers reliable, scalable, and secure data solutions that power fraud detection, prevention, analytics, and decision-making across Navy Federal Credit Union. Our mission is to enable data-driven fraud strategies by providing high-quality data, modern data platforms, and engineering solutions that support Fraud Operations, Fraud Strategy & Reporting, Fraud Detection, Fraud Data Science and Machine Learning models & Fraud Investigation teams.

Requirements

Must-haves

Assist in data readiness for AI use by supporting transformation, data quality, and accessibility efforts that enable AI and machine learning solutions.
Collaborate with Data Engineers, Data Scientists, Fraud Analysts, and business stakeholders on data-driven projects.
Collaborate with cross-functional teams to understand business challenges and translate them into data-driven solutions.
Collaborate with engineers, analysts, and business partners to gather requirements and deliver solutions.
Assist in creating business requirement documents, build STTMs, testing, and validating the fraud and security-related data.
Enable real-time and batch data integration across enterprise systems and external fraud data providers.
Support fraud business teams with timely, accurate, and actionable data solutions.
Analyze data to identify quality issues, trends, and opportunities for improving operational efficiency.
Document technical processes, data flows, and engineering solutions.
Participate in Agile team activities, including planning sessions, standups, and project reviews.
Contribute to the development of Databricks Notebooks, enhance UAT testing & data validation framework.
Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, Information Systems, Software Engineering, Analytics, or a related field.
Experience with programming languages such as Python, SQL, Scala, Java, or similar technologies.
Familiarity with relational databases, data warehousing concepts, and data modeling.
Basic understanding of ETL/ELT processes and data integration concepts.
Strong analytical and problem-solving skills with the ability to work with large and complex datasets.
Strong communication, collaboration, and technical presentation skills.
Additional Information
Monday – Friday, 8:00 AM – 4:30 PM
820 Follin Lane SE, Vienna, VA 22180

Nice-to-haves

Knowledge of cloud platforms (Azure, AWS, or GCP) is preferred.
Familiarity with big data technologies such as Databricks, Spark, Kafka, or Hadoop is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiaccessibilityagilepythonsqljavaazureawsgcp

Platform: oracle-cloud
Effort: medium

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