About the role
We are seeking a highly skilled Senior Security Data Engineer to design, build, and optimize large-scale security data pipelines and analytics platforms. The ideal candidate will have strong expertise in Cribl, Snowflake, Python, SQL, security analytics, SIEM technologies, and cloud platforms. The role involves developing scalable data ingestion frameworks, processing security telemetry, and enabling advanced threat detection and security analytics.
The core responsibilities for the job include the following:
Security Data Engineering:
- Design and build large-scale security data ingestion, transformation, and enrichment pipelines.
- Develop solutions for collecting and processing logs from multiple security platforms.
- Create scalable data models for security analytics and reporting.
- Implement data quality, validation, error handling, and monitoring frameworks.
- Support security data lake and data warehouse initiatives.
Data Pipeline Development:
- Build batch and real-time ETL/ELT pipelines using Python and SQL.
- Integrate data from various security and cloud sources.
- Optimize Snowflake-based data processing and storage.
- Develop data normalization and enrichment processes.
Security Analytics and SIEM:
- Support SIEM/SOAR use cases through security data engineering.
- Enable security telemetry collection and analytics.
- Design data pipelines for threat detection, incident response, and cyber analytics.
- Collaborate with Security Operations and Detection Engineering teams.
Cloud and Streaming Technologies:
- Work with AWS, Azure, or GCP environments.
- Develop integrations using Kafka, Kinesis, or similar streaming technologies.
- Process cloud-native logs and security events.
Collaboration and Innovation:
- Work closely with security architects, SOC teams, and data engineers.
- Adopt AI-assisted development tools such as GitHub Copilot, ChatGPT, Gemini, or similar.
- Participate in code reviews, architecture discussions, and performance tuning activities.