Fort Meade, MD onsite role for an advanced Security Data Analytics Engineer focused on cryptographic discovery and quantum-vulnerability detection.

Architect and optimize real-time telemetry ingestion pipelines using Apache Kafka to support high-throughput data flow.
Develop large-scale transformation and enrichment workflows on Databricks and Apache Spark, ensuring pipeline reliability, observability, and enterprise-grade performance.
Maintain data quality, schema governance, and lineage documentation across all pipeline stages.
Implement custom detection algorithms targeting non-compliant cryptographic protocols, including outdated TLS versions, weak SSH key configurations, and deprecated cipher suites.
Build detection logic for quantum-vulnerable algorithms (such as RSA and ECC) across enterprise network traffic and cryptographic inventories.
Continuously tune detection models based on operational feedback, false positives, and evolving threat intelligence.
Partner with DoW threat hunters to translate raw cryptographic discovery data into structured, analyst-ready datasets.
Build and maintain interactive dashboards in Tableau and Grafana for executive reporting on cryptographic posture and quantum-vulnerability exposure.
Develop and document analytic methodologies; produce findings briefs and technical reports for government stakeholders.

Active TS/SCI clearance (no exceptions).
5+ years of data engineering or security analytics experience, including production-grade pipeline development.
Advanced proficiency in SQL and Python — including pandas, PySpark, and data manipulation at scale.
Hands-on experience with Databricks and/or Apache Spark for large-scale data transformation.
Working knowledge of Apache Kafka for real-time streaming ingestion.
Deep familiarity with security log formats, cryptographic protocols (TLS, SSH, PKI), and network telemetry.

Security Data Analytics Engineer TS/SCI

Job Description