Data Management and Analytics Engineer

Position Summary

MAG is staffing for a Data Management and Analytics Engineer to transform raw data into strategic intelligence through advanced analytics and data engineering. You'll architect scalable data solutions using the SAS platform, enabling data‑driven decision making that directly supports mission‑critical operations and business objectives.

U.S. Citizens Only

Former US Defense Contractor / US Gov / US Military Experiences Only

Hybrid Position – Remote primarily; occasional on‑site at MAG office when requested.

Location Preference – Candidates living in proximity to our corporate HQ in Fairfax, VA, or near satellite offices in Aberdeen, MD;
Titusville, FL;
Newport News, VA;
Carthage, NC.

• Design and implement robust data pipelines using SAS Viya and SAS Data Integration Studio.
• Lead migration initiatives from legacy data environments to SAS Viya cloud instances, including comprehensive data rationalization and modernization of existing algorithms for cloud‑native computing architectures.
• Develop advanced SAS programming solutions (Base SAS, SAS/STAT, SAS Macros, SAS/SQL) for complex data transformations.
• Build statistical models and machine learning algorithms using SAS Enterprise Miner and SAS Visual Analytics.
• Create automated ETL processes to extract, cleanse, and transform large datasets from diverse sources.
• Implement data quality frameworks and governance standards to ensure accuracy and integrity.
• Develop interactive dashboards and visualizations using SAS Visual Analytics and other BI tools.
• Optimize data warehouse architecture and implement efficient data modeling principles.

• Create comprehensive reports and presentations to communicate analytical findings to stakeholders.
• Collaborate with business analysts and data scientists to translate requirements into technical solutions.
• Maintain and enhance existing SAS code libraries for efficiency and reusability.
• Provide technical mentoring and knowledge transfer to junior team members.
• Support data governance initiatives and compliance with industry regulations.
• Implement data security protocols and access controls within SAS environments.
• Document analytical processes and maintain comprehensive technical specifications.

• 5+ years of hands‑on experience in data management, data engineering, or analytics.
• Extensive, proven experience with SAS Viya platform including SAS Enterprise Guide and SAS Data Integration Studio.
• Expert‑level SAS programming skills with focus on data manipulation, ETL processes, and statistical analysis.
• Strong proficiency in SQL for querying and manipulating relational databases (Oracle, SQL Server, Postgre
  
  SQL).
• Experience with data warehousing concepts, dimensional modeling, and database optimization.
• Strong scripting skills (Python, R, or advanced SAS macro programming).
• Understanding of statistical analysis, data mining, and machine learning techniques.
• Experience with cloud platforms (Azure required, AWS/GCP beneficial).
• Knowledge of data visualization tools (SAS Visual Analytics, Tableau, Power BI).

• SAS Global Certifications (Base Programming, Advanced Programming, Data Integration).
• Experience with SAS Visual Analytics and SAS Enterprise Miner.
• Knowledge of machine learning frameworks and advanced statistical modeling.
• Familiarity with big data technologies (Hadoop, Spark, No
  
  SQL databases).
• Experience with version control systems (Git) and collaborative development practices.
• Understanding of data privacy regulations and compliance frameworks.
• Experience with the Department of Defense or similarly regulated environment.

• Bachelor's degree in Computer Science, Statistics, Mathematics, or related quantitative field.
• Preferred – Master's degree in Statistics, Data Science, or related analytical field.

• Must be eligible for Secret security clearance.
• U.S. Citizenship required.

• You turn complex datasets into actionable insights.
• You build scalable, maintainable data solutions, not one‑off analyses.
• You implement data quality into pipelines, not validate…