Data Engineer

Own accuracy and coverage of detection capabilities such as: technology fingerprinting, SSO/login identification, vulnerability-to-technology mappingBuild AI-powered workflows (LLM classifiers, automated rule generation) to scale detection beyond manual rule-writingDesign eval frameworks and feedback loops - golden datasets, precision/recall tracking, regression testing - to keep quality high as automation growsTune IP/domain discovery logic: decision rules, blacklists, thresholds, unstructured data parsingExtend BI pipelines and schemas to enrich asset dataInvestigate customer-reported detection gaps; root-cause and fix systematicallyRequirements: Python (scripting, automation, data analysis)Hands-on AI/LLM engineering - building multi-step pipelines with LLM APIs, not just promptingEvaluation mindset - experience measuring and maintaining accuracy of automated systemsRegex, SQL, ETL conceptsWeb fundamentals (HTML, HTTP, JS) and basic networking (DNS, WHOIS, CIDR)Familiarity with CVE/CPE vulnerability ecosystemGit workflowThis position is open to all candidates.