Navy Federal Credit Union - Vienna, VA

posted 3 months ago

Full-time - Mid Level
Vienna, VA
Credit Intermediation and Related Activities

About the position

As a key member of the Guardian Risk Hub team, you will partner with business stakeholders, technology teams, and other analysts to understand the critical data needs of the organization and its integrations with various platforms. This includes working with systems such as the Operational Data Hub, S3, LexisNexis, Alloy Data, mainframe systems, Splunk, EWS, Senilink, Ensenta, Encore, Federal Reserve, MuleSoft API, ACE Workflow, and FIS Mobile Check Data. Your role will involve capturing and refining data requirements, performing complex data analysis, and contributing to the design and optimization of data pipelines and integrations. You will be instrumental in driving data-driven insights and ensuring data accuracy and integrity across the Guardian ecosystem, supporting both departmental and organizational objectives. In this position, you will work independently on complex business challenges that have a significant impact on the company's data strategy. You will collaborate with technology teams and business end-users to define data and analysis needs, develop technical requirements, including data definitions, business rules, and data quality standards, and ensure the successful integration of the Guardian Risk Hub with various data sources. Your responsibilities will also include leading cross-functional data collection efforts, developing and implementing strategies to gather, clean, and preprocess data from diverse sources, ensuring accuracy, consistency, and completeness across the Guardian Risk Hub and its connected platforms. You will conduct data validation, integrity research, and user acceptance testing (UAT) to ensure that data from all integrated systems meets the stringent quality standards required for risk analysis and fraud detection. Additionally, you will perform in-depth data analysis to support complex business intelligence and data science projects, focusing on predicting, improving, and measuring the success of key business-to-business outcomes related to risk and fraud management. Your expertise will be crucial in resolving complex data quality issues using advanced data profiling, cleansing, and monitoring techniques to maintain data integrity and reliability across multiple integrated systems, including both traditional and cloud-based platforms.

Responsibilities

  • Collaborate with technology teams and business end-users to define data and analysis needs, develop technical requirements, including data definitions, business rules, and data quality standards.
  • Ensure the successful integration of Guardian Risk Hub with various data sources, including mainframe systems, Splunk, EWS, Senilink, Ensenta, Encore, Federal Reserve, and FIS Mobile Check Data.
  • Lead cross-functional data collection efforts, developing and implementing strategies to gather, clean, and preprocess data from diverse sources, ensuring accuracy, consistency, and completeness across the Guardian Risk Hub and its connected platforms.
  • Conduct data validation, integrity research, and user acceptance testing (UAT) to ensure that data from all integrated systems meets the stringent quality standards required for risk analysis and fraud detection.
  • Perform in-depth data analysis to support complex business intelligence and data science projects, focusing on predicting, improving, and measuring the success of key business-to-business outcomes related to risk and fraud management.
  • Resolve complex data quality issues using advanced data profiling, cleansing, and monitoring techniques to maintain data integrity and reliability across multiple integrated systems, including both traditional and cloud-based platforms.
  • Generate descriptive statistics and organize raw data to support ongoing business intelligence and data science efforts, ensuring that insights are actionable and aligned with business goals across all integrated data sources.
  • Partner closely with data science teams to maintain and enhance statistical models used in risk analysis and fraud detection, providing ongoing support for both ad hoc and routine data reviews, especially concerning data integration from varied sources.
  • Contribute to the continuous improvement of data governance practices, procedures, and standards, with a focus on ensuring data quality and integrity across all Guardian Risk Hub integrations, including legacy systems and modern APIs.
  • Develop and maintain strong working relationships with cross-functional teams, subject matter experts, and senior leaders, serving as a trusted advisor on complex data issues and initiatives across all integrated systems.
  • Lead and manage moderate to large data projects, driving initiatives that enhance data quality and integration across the Guardian Risk Hub and its connected platforms, ensuring seamless data flow between systems like mainframe, Splunk, and cloud services.
  • Mentor and guide junior analysts, providing expert advice and fostering a culture of data excellence within the team.

Requirements

  • 7-10 years of experience in data analysis, with a strong focus on data integration and reporting across multiple platforms, including legacy systems and cloud-based solutions.
  • Subject matter expert in data analysis with a deep understanding of the interrelationships between different data disciplines, particularly in the context of risk and fraud management, utilizing data from diverse sources such as Splunk, EWS, and MuleSoft API.
  • Expert proficiency in data manipulation and scripting languages such as SQL, Python, or R, with advanced skills in data cleaning, preprocessing, and integration techniques across various data platforms, including mainframes and modern APIs.
  • Advanced expertise with ETL tools and techniques, and experience with data integration across diverse data sources, including mainframes, cloud platforms like S3, and API-based integrations.
  • Strong background in interpreting, extrapolating, and interpolating data for complex statistical research and modeling, particularly in risk and fraud analytics, using data from sources such as LexisNexis, Encore, and FIS Mobile Check Data.
  • Demonstrated knowledge of data governance principles and practices, with experience establishing and enforcing robust data governance policies across integrated data platforms, including legacy and modern systems.
  • Bachelor's Degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.

Benefits

  • Highly competitive pay
  • Generous benefits and perks
  • Recognition as one of the Best Companies for Latinos to Work for 2024
  • Ranked in Computerworld® Best Places to Work in IT
  • Listed in Forbes® 2024 America's Best Large Employers
  • Recognized in Forbes® 2023 The Best Employers for New Grads
  • Included in Fortune Best Workplaces for Millennials™ 2023
  • Ranked in Fortune Best Workplaces for Women ™ 2023
  • Listed in Fortune 100 Best Companies to Work For® 2024
  • Recognized by Military Times 2023 Best for Vets Employers
  • Awarded Newsweek Most Loved Workplaces
  • Received Ripplematch Campus Forward Award - Excellence in Early Career Hiring
  • Ranked in Yello and WayUp Top 100 Internship Programs
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