Staff Security Detection Engineer

Databricks Databricks · Data AI · United States · Security

Staff Security Detection Engineer at Databricks responsible for designing and implementing scalable intrusion detection solutions using machine learning and log analysis. The role involves optimizing log ingestion, developing anomaly-based and ML-driven detection strategies, and integrating these with the Databricks platform. It requires strong software engineering skills, cloud security expertise, and familiarity with distributed computing and ML.

What you'd actually do

  1. Design and implement advanced detection strategies by deeply understanding and analyzing new or unknown log sources, schemas, and raw data.
  2. Collaborate with cross-functional teams, including product and data engineering teams, to build efficient log ingestion pipelines and support large-scale data analytics.
  3. Engineer and deploy detection solutions on Databricks using Spark, Python, and other cutting-edge technologies with a strong emphasis on clean code, rigorous testing, and comprehensive documentation.
  4. Develop Rule-based and/or ML-based intrusion detection models and integrate them with Databricks' platform, ensuring high accuracy and minimal false positives.
  5. Partner with Incident Response teams to perform threat hunting and to provide detailed logging, alerts, and playbooks, empowering proactive threat detection and response.

Skills

Required

  • Python
  • Git/GitHub
  • CI/CD automation
  • network security
  • cloud security
  • application/log analysis
  • endpoint security
  • distributed computing environments (e.g., Pyspark)
  • SQL
  • data analysis tools
  • machine learning
  • securing and operating at least one major cloud environment (AWS, Azure, GCP)

Nice to have

  • terraform knowledge

What the JD emphasized

  • deeply understanding and analyzing new or unknown log sources
  • ML-based intrusion detection models
  • threat hunting
  • security detection engineering
  • detection engineering

Other signals

  • ML-driven detection strategies
  • anomaly-based detection
  • intrusion detection models
  • threat hunting
  • detection engineering