Job DescriptionACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIREDWe are seeking a
Production Analytics Engineer to join our technical operations team. In this role, you will develop, optimize, and sustain production-grade analytics utilizing Python. You will provide direct analytic support for mission-critical pipelines, transforming complex network data into reliable, actionable intelligence products.
The annual base salary range for this role is $213,000-$252,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
Required SkillsProficiency in Python: Demonstrated software engineering experience writing, testing, and debugging production-grade Python scripts and analytic workflows.
Network Fundamentals & Protocols: Strong foundational knowledge of network protocols, packet structures, flow metadata (e.g., NetFlow/IPFIX), and common Internet architectures (TCP/IP, DNS, BGP).
Production Analytic Support: Proven track record of monitoring, troubleshooting, and maintaining data analytics in live, sustained operational environments.
Data Serialization & Querying: Solid experience working with common data formats (JSON, XML, CSV) and relational/non-relational database querying (e.g., SQL, NoSQL).
Education:
Bachelor's Degree in Cyber Security, Computer Science, Computer Engineering, or a related technical discipline, PLUS
11+ years of professional experience in cyber operations, network analysis, or data engineering OR
Associate's Degree PLUS
13+ years of specialized technical experience in lieu of a degree.
Desired Skills- Analytic Tooling & Frameworks: Familiarity with mission analyst ecosystems and querying interfaces (e.g., DX, XKEYSCORE, or related intelligence tools).
- Distributed Data Processing: Familiarity with distributed computing and messaging systems such as Apache Spark, Kafka, or Hadoop.
- CI/CD & Version Control: Experience maintaining analytic codebases using Git, GitLab CI/CD, Jenkins, and automated testing frameworks.
- Containerization: Practical experience deploying and managing workloads within Docker containers or Kubernetes environments.
- Agile Team Collaboration: Experience working within an Agile/Scrum team collaborating directly with mission stakeholders, data scientists, and infrastructure engineers.
