Data Specialist

• Managing cognitive interview and research data, including ingestion, cleaning, coding, and secure storage for video, audio, and text assets.
• Designing and operating data models and pipelines that support QVideo/QNotes/QBankstyle repositories and downstream analysis.
• Implementing data migration from legacy systems to COTS/LCNC/.NET Azure solutions, including mapping, validation, and reconciliation.
• Applying data governance, privacy, and security controls in federal or regulated environments (handling PII under strict policies).
• Building data access layers, queries, and APIs using .NET and Azure data services (e.g., Azure SQL/Managed Instance, Blob Storage).
• Supporting SDLC and change control by defining data requirements, test cases, and acceptance criteria for datarelated changes.

• 7-10+ years in data management, data engineering, or database development.
• 3-5+ years working with sensitive data in federal/healthcare/regulated environments.
• 3-5+ years with SQL/.NET data stack, and 2-3+ years using Azure data services; 1-2+ years integrating with LCNC (e.g., Appian) is a plus.

The Manhattan Strategy Group (MSG) LLC, a rapidly growing management consulting firm, is seeking a Data Specialist for an upcoming opportunity for National Center for Health Statistics (CDC). MSG provides consulting services across the federal government in the areas of training and technical assistance, grants management, research and evaluation, marketing and communications, program management, and information technology. Our federal clients include the U.S. Departments of Labor, Education, Health and Human Services, Agriculture, Justice, and Homeland Security., Manhattan Strategy Group (MSG) is an Equal Opportunity Employer and considers all qualified applicants for employment without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other characteristic protected by law. Our hiring decisions are based solely on merit and qualifications, ensuring a fair and competitive selection process.