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Operations Research / Systems Analyst (ORSA) - Data Engineering and Analytics (5406)

SMX
CompanySMX
CategoryEngineering
LocationHanover
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted30 Jun 2026
Last verified4 Aug 2026
SourceEmployer ATS (greenhouse)
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Description
The Operations Research (OR) Analyst - Data Engineering & Analytics Focus provides advanced data integration, architecture, and analytics capabilities to enable quantitative analysis across a Federal Agency's personnel vetting, industrial security, and counterintelligence operations. This position is responsible for transforming disparate, complex data sources into unified analytical datasets that enable the Federal Agency to transition from reactive reporting toward proactive, mathematically grounded forecasting and optimization. Essential Duties & Responsibilities Data Integration & Architecture Design and implement data architectures that integrate information from multiple disparate source systems supporting personnel vetting, industrial security, and counterintelligence missions Build and maintain ETL pipelines that extract, transform, and load data from heterogeneous sources including relational databases, flat files, APIs, and legacy systems Develop entity resolution approaches to link person, company, and facility records across systems that lack common unique identifiers Create reusable data integration frameworks that accommodate changes in source system schemas and evolving business rules Data Quality & Validation Develop data quality frameworks that assess completeness, accuracy, consistency, and timeliness of source data Implement data validation processes that identify and handle missing data, duplicates, temporal inconsistencies, and conflicting information Document data provenance, transformations, quality metrics, and known limitations to ensure analytical reproducibility Collaborate with system owners and data stewards to resolve data quality issues at the source Analytics & Visualization Apply statistical analysis and exploratory data analysis to understand operational patterns and trends within integrated datasets Develop interactive dashboards and visualization products that communicate complex data insights to technical and non-technical audiences Translate data findings into objective, data-driven insights that support strategic and operational decision-making Create data marts and analysis-ready datasets optimized for specific use cases (process modeling, risk assessment, resource optimization) Collaboration & Support Partner with modeling/simulation and optimization specialists to define analytical dataset requirements Support advanced analytics by providing clean, well-documented, empirically grounded datasets Participate in cross-functional team activities to maintain technical standards and share knowledge Adapt analytical approaches to work within data availability constraints while maintaining analytical rigor   Required Skills/Experience 8+ years of progressive, hands-on operations research or data analytics experience, including demonstrated application of statistical analysis, data integration, and quantitative methods to real-world operational problems 3–5 years of that experience supporting DoD or Intelligence Community mission areas such as personnel vetting, industrial security (NISP), counterintelligence, or insider threat Expert-level proficiency in SQL and relational database design, including complex queries, joins, subqueries, and performance optimization Demonstrated, hands-on proficiency in an analytical programming language (Python or R), including libraries for data manipulation (pandas, dplyr), analysis, and visualization Proven experience building ETL pipelines and data integration workflows across multiple disparate data sources Experience with data architecture for analytics (data warehousing, dimensional modeling, data lakes) Experience assessing and improving data quality in complex, multi-source operational environments Ability to translate technical data challenges into clear communication for non-technical stakeholders Experience working with data in