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

SMX•Hanover, MD

1d•$123,000 - $185,000

About The Position

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.

Requirements

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 secure (classified) government environments
Secret clearance required (active or ability to obtain)

Nice To Haves

Advanced degree in Operations Research, Applied Mathematics, Statistics, Computer Science, Data Science, Information Systems, or a related quantitative discipline
Familiarity with NISP, clearance adjudication processes, and/or insider threat/counterintelligence analytic frameworks
Experience with entity resolution, record linkage, and master data management techniques
Familiarity with both legacy government systems and modern cloud-based data architectures
Experience with version control and collaborative development (Git/GitHub)
Knowledge of data governance frameworks, metadata management, and data cataloging
Experience with big data technologies (Spark, distributed computing)
Familiarity with NoSQL databases (MongoDB, Neo4j, graph databases)
Data visualization tools (Tableau, Power BI, Plotly, D3.js)
Experience with data orchestration tools (Airflow, Prefect, Luigi)
API development and integration experience (REST, SOAP)
Knowledge of queueing theory, statistical modeling, or optimization methods to better support analytical consumers

Responsibilities

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
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
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)
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