Senior Security Data Engineer
New
Remote-first work environment across the continental United StatesFull-TimeSenior
Salary153,000 - 212,000 USD per year
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Job Details
- Experience
- 5+ years of experience
- Required Skills
- PythonSQLGo
Requirements
- 5+ years of experience in software engineering or data-focused roles with a strong emphasis on security, analytics, or large-scale data systems.
- Strong programming skills in Python, Go, or similar languages, along with solid experience using SQL for large dataset analysis.
- Proven ability to transform ambiguous, large-scale data into structured, actionable insights for security or analytical use cases.
- Strong understanding of Internet-scale systems and security-relevant data such as exposed hosts, services, and infrastructure behavior.
- Excellent collaboration and communication skills, with experience working alongside engineers, researchers, and security professionals.
- Analytical mindset with strong problem-solving abilities and attention to detail in data interpretation and modeling support.
- Familiarity with concepts such as classification, clustering, anomaly detection, or feature engineering is a strong plus.
- Experience with Internet protocols (HTTP, DNS, TLS, SSH, PKI) or security/telemetry systems is highly desirable.
Responsibilities
- Analyze large-scale Internet telemetry and derived datasets to identify meaningful patterns and signals that improve machine learning models for security classification.
- Design, build, and maintain high-quality training and evaluation datasets using raw telemetry, curated labels, and expert-reviewed security data.
- Develop feature engineering, labeling strategies, and data pipelines that support classification of entities such as benign, suspicious, or malicious infrastructure.
- Collaborate with security researchers and detection teams to translate domain expertise into structured, model-ready data and workflows.
- Partner with ML and software engineers to ensure scalable, production-ready integration of features, labels, and datasets.
- Contribute to evaluation frameworks that improve model performance metrics such as precision, recall, and coverage over time.
- Build internal tooling and automation to support large-scale data processing, labeling, and feature discovery efforts.
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