Data Analyst (Architecture)

Seneca Holdings•Washington, DC

About The Position

Great Waters Federal i s part of the Seneca Nation Group (SNG) portfolio of companies . SNG is Seneca Holdings' federal government contracting business that meets mission-critical needs of federal civilian, defense, and intelligence community customers. Our portfolio comprises multiple subsidiaries that participate in the Small Business Administration 8(a) program. To learn more about SNG, visit the website and follow us on LinkedIn . Our team of talented individuals is what makes us successful. To support our team, we provide a balanced mix of benefits and programs. Your total rewards package includes competitive pay, benefits, and perks, flexible work-life balance, professional development opportunities, and performance and recognition programs. We offer a comprehensive benefits package that includes medical, dental, vision, life, and disability, voluntary benefit programs (critical illness, hospital, and accident), health savings and flexible spending accounts, and retirement 401K plan. One of our fundamental principles is to offer competitive health and welfare benefits to our team members, providing coverage and care for you and your family. Full-time employees working at least 30 hours a week on a regular basis are eligible to participate in our benefits and paid leave programs. We pride ourselves on our collaborative work environment and culture, which embraces our mission of providing financial and non-financial benefits back to the members of the Seneca Nation. This position is to provide business analyst support services for the National Student Loan Data System (NSLDS) Data Quality & Monitoring contract implementing the requirements for the One Big Beautiful Bill Act to assist FSA in the areas of enhancing and streamlining existing data integrations, improving the quality and utility of existing data assets and school-facing and borrower-facing products, optimizing the ability to deliver reports and insights to FSA and FSA stakeholders, and ensuring compliance with data access. Along with facilitating collaboration, optimizing resource allocation, managing risks, streamlining processes, and integrating system requirements across business units to ensure successful execution of our multifaceted projects. Great Waters Federal is seeking a Data Analyst (Architecture) in the National Capital Region. The objective of this work is to provide from professionals with substantial experience in the development, improvement, and management of operational data within the federal government. Additionally, FSA requires skilled analysts to conduct ongoing data reviews, ensuring the completeness and accuracy of data loaded and stored in FSA systems. These analysts should possess advanced skills and be capable of quickly understanding FSA's data and the intricacies of system collaboration with other FSA and partner systems for complex business and program processes.

Requirements

Bachelor's degree in Business, IT, Finance, or related field
U.S. citizens with High-Risk Public Trust
7+ years in data engineering, analytics, or architecture, with proven leadership
Experience with SQL, Python, data modeling (dimensional, Data Vault), and cloud platform (AWS/Azure/GCP) skills, with deep experience in designing data warehouses/lakes, ETL/ELT, and streaming data
Highly skilled individual who can quickly understand the NSLDS data model and the intricacies of NSLDS’s collaboration with other FSA and Federal Loan Servicer systems for complicated business and program processes.
Experience with translating complex business needs into robust data architecture and system requirements.
Experience with designing, building, and managing enterprise data models, pipelines, and platforms.
Analytical Experience with data analysis, critical thinking, problem-solving, process modeling.
Excellent written/verbal skills, translating technical info, stakeholder management.

Nice To Haves

Familiarity with NSLDS, other FSA systems, and FSA enterprise architecture preferred
Knowledge of Federal Student Aid business areas to include, but not limited to, Federal Loan Servicing data, repayment plans, Gainful Employment regulations and other accountability metrics, and more
Knowledge of or ability to quickly understand business processes and integrations across FSA systems to accomplish a business goal; for example, Income Driven Repayment and SAVE processes as documented across multiple systems including NSLDS, Common Origination and Disbursement (COD) system, Federal Loan Servicers (FLS), Federal Tax Information Model (FTIM) systems, and more

Responsibilities

Responsible for the correct and proper fulfillment of production and ad hoc analytics and reporting requirements.
Oversees query writing staff, data science support staff, and contracting staff engaged in supporting FSA statistical model development and implementation.
Ensures data used by an organization is accurate, consistent, and complete.
Define data terminology by creating data dictionaries.
Monitor data quality to ensure consistency.
Reviews data sets, analyzes trends, and addresses any issues promptly.
Create reports detailing data quality issues and resolutions for senior management.
Recommend changes to enhance data storage efficiency and accuracy.
Analyze test results to ensure data implementation aligns with program intents, objectives, and stated requirements.
Review outstanding Data Quality JIRA/ACERS tickets and develop recommendations for remediation of identified issues.
Evaluate current data edits and controls and provide recommendations to enhance data quality, including changes to data provider instructions and procedures.
Create comprehensive dashboards, detailed reports, and narratives on data quality.
Review and recommend requirements for refining and/or creating data models supporting FSA's operational needs, aligning them with federal government standards.
Recommend best practices in data management, maintain comprehensive documentation, and ensure data integrity, security, and usability.
Perform data reviews in FSA systems, identify discrepancies, and resolve them promptly.
Implement data quality standards and metrics, generate reports on data quality, and provide training to FSA staff on data review processes and best practices.