Job description

The Fraud Data Engineering & Governance 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, technical, and program support to the Fraud Data Governance team by assisting with metadata management, data quality, data profiling, and system analysis. The intern will gain hands-on experience working with large-scale fraud datasets, cloud technologies, and modern data governance tools used to support the fraud prevention and detection data ecosystem. Potential Summer Associate projects may include: • Assist in data readiness for AI use by supporting transformation, data quality, governance, and accessibility efforts that enable AI and machine learning solutions. • Collaborate with peer Data Analysts and 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. • Work with internal and enterprise partners to advance the department’s AI Governance framework and structures. 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. • Support fraud business teams with timely, accurate, and actionable data solutions. • Analyze data to identify quality issues, trends, and opportunities for improving operational efficiency. • Coordinate with enterprise partners, such as Enterprise Data Governance, Data Risk Governance, and Enterprise Technology, to advance data governance initiatives. • Document technical processes, data flows, and governance outcomes. • Participate in Agile team activities, including planning sessions, standups, and project reviews. • Contribute to the development of Databricks Notebooks, enhance DQ monitoring/ testing, and data validation framework • Currently pursuing a master’s degree in Data Analytics, Data Engineering, Data Science, Information Systems, Information Technology Management, MBA, 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. • Familiarity with core data governance principles, including metadata management, data lifecycles, data quality, record retention, and privacy. • 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 & Governance 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, governance, and accessibility efforts that enable AI and machine learning solutions.
Collaborate with peer Data Analysts and 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.
Work with internal and enterprise partners to advance the department’s AI Governance framework and structures.
Support fraud business teams with timely, accurate, and actionable data solutions.
Analyze data to identify quality issues, trends, and opportunities for improving operational efficiency.
Coordinate with enterprise partners, such as Enterprise Data Governance, Data Risk Governance, and Enterprise Technology, to advance data governance initiatives.
Document technical processes, data flows, and governance outcomes.
Participate in Agile team activities, including planning sessions, standups, and project reviews.
Contribute to the development of Databricks Notebooks, enhance DQ monitoring/ testing, and data validation framework
Currently pursuing a master’s degree in Data Analytics, Data Engineering, Data Science, Information Systems, Information Technology Management, MBA, 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.
Familiarity with core data governance principles, including metadata management, data lifecycles, data quality, record retention, and privacy.
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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