ParsonsKellogg is seeking an RF Data Engineer with expertise in DoD spectrum management and data engineering to support their Drone Armor C-UAS program. The role involves analyzing RF spectrum data, developing data pipelines, and ensuring compliance with military spectrum certification processes.
Responsibilities:
- Analyze RF spectrum data to identify and characterize drone communication signatures and control frequencies
- Develop and maintain spectrum certification documentation supporting Drone Armor system deployments
- Coordinate frequency assignments and deconfliction efforts with military and civilian spectrum management authorities
- Generate technical documentation including DD Form 1494 for Drone Armor RF operations
- Conduct electromagnetic interference (EMI) and electromagnetic compatibility (EMC) analysis for C-UAS operations
- Design and implement data pipelines for real-time RF signal collection, processing, and analysis from Drone Armor sensors
- Develop machine learning models to improve drone detection accuracy and reduce false alarm rates
- Create data visualization dashboards for operational monitoring and system performance assessment
- Apply advanced analytics to RF spectrum data to identify emerging drone threats and communication protocols
- Implement cloud-based data architectures for scalable storage and processing of RF sensor data
- Support field testing and evaluation of Drone Armor systems in operational environments
- Perform frequency-dependent analysis to assess system effectiveness across various RF bands
- Collaborate with software development teams to integrate RF data processing algorithms
- Utilize spectrum databases (Spectrum XXI, JSDR, EL-CID) to support deployment planning
- Conduct Spectrum Supportability Risk Assessments (SSRAs) for Drone Armor acquisitions
- Prepare technical reports documenting RF performance, spectrum utilization, and system capabilities
- Support Host Nation Coordination activities for international Drone Armor deployments
- Assist with Equipment Spectrum Certification processes per AR 5-12 and DoD 5000.1
- Maintain awareness of evolving drone technologies and RF communication standards
Requirements:
- Master's degree in computer science, electronics engineering OR other engineering or technical discipline is required with 10 yrs of experience OR 10 years of relevant RF/data engineering experience may be substituted for education
- Minimum ten (10) years of experience with Department of Defense (DoD) spectrum management policy, certification, and documentation
- Minimum eight (8) years of experience in generating DD Form 1494 and reviewing detailed spectrum certification documents
- Minimum four (4) years of experience with data engineering, data analytics, or software development
- Proficiency in programming languages such as Python, MATLAB, R, or similar for data analysis
- Experience with RF measurement equipment and spectrum analyzers
- Knowledge of wireless communication protocols and RF propagation principles
- Familiarity with DoD spectrum management policies and procedures
- Understanding of data engineering principles including ETL processes, data modeling, and database management
- Experience with cloud technology platforms (AWS, Azure, Google Cloud) and their application to data engineering workflows
- Understanding of data mesh architectures and data tagging methodologies
- Familiarity with electronic phenomena principles as they relate to RF systems and spectrum management
- Must be a US Citizen
- SECRET security clearance is required
- PhD in Electrical Engineering, Computer Science, Data Science, or related discipline
- Professional certifications in data science, cloud computing (AWS/Azure/GCP), or RF engineering
- Experience with counter-drone technologies, electronic warfare, or signals intelligence (SIGINT)
- Knowledge of commercial and military drone RF signatures and communication protocols
- Experience with machine learning/AI applications for RF signal classification
- Familiarity with software-defined radio (SDR) platforms and development
- Knowledge of NTIA Redbook, ITU regulations, and CJCSM 3320.01
- Experience with big data technologies (Hadoop, Spark, Kafka) for RF data processing
- Proficiency in cloud platforms (AWS, Azure, Google Cloud) for data engineering workflows
- Experience with data mesh architectures and modern data governance practices
- Knowledge of containerization (Docker, Kubernetes) and DevOps practices
- Experience with real-time data streaming and edge computing applications
- Familiarity with digital signal processing (DSP) techniques and algorithms
- Experience with software coding for data interoperability between DoD spectrum management systems
- Experience accessing spectrum databases: Spectrum XXI, HNSWDO, JSDR, Stepstone, EL-CID
- Proficiency with data visualization tools (Tableau, Power BI, Grafana)
- Experience with version control systems (Git) and collaborative development environments
- Experience performing Frequency Dependent Rejection and Frequency Distance Separation Analysis
- Knowledge of statistical analysis and hypothesis testing methodologies
- Experience with electromagnetic modeling and simulation tools