Senior Risk & Compliance Engineer

Responsibilities

• Build automated signal ingestion pipelines that pull real-time data from security tooling — normalizing, enriching, and scoring raw findings into actionable, ranked risk intelligence that drives remediation decisions across the organization.
• Develop probabilistic risk models that express security exposure as probability distributions, giving leadership a quantified, confidence-backed view of breach likelihood and expected losses — connecting model outputs directly to investment decisions and board-level reporting.
• Identify systemic choke points across the attack surface — high-leverage remediation paths where a single fix eliminates risk at scale — and prioritize them by expected impact to maximize the efficiency of our security program.
• Build dashboards and data-driven insights that cascade risk visibility across security, engineering, and executive stakeholders, translating complex model outputs into language that resonates at every level of the organization.
• Support risk quantification efforts that express security exposure in financial terms, connecting model outputs to investment decisions and board-level reporting.

Requirements

• 5+ years of experience in data engineering, with demonstrated ability to write production-level code in Python and SQL (PostgreSQL, Presto, or SparkSQL).
• 3+ years of experience building and deploying machine learning or probabilistic models (e.g., Bayesian models) in a production environment.
• Experience building data pipelines that ingest real-time or near-real-time data across multiple formats, handling both stream and batch processing at scale.
• Experience with data modeling for classification, normalization, and risk or anomaly detection signal development.
• Experience developing metrics that inform security and business decisions.

Nice-to-Haves

• Familiarity with security risk concepts including threat intelligence enrichment pipelines, EPSS, or CISA KEV.
• Familiarity with quantitative risk frameworks such as FAIR.
• Exposure to security frameworks such as NIST CSF, SOC 2 as context for what controls the data is measuring.
• Demonstrated ability to translate risk model outputs into executive or board-level narratives.
• A genuine passion for building systems that protect customers and products, and a track record of operating effectively in fast-paced, ambiguous environments where the program is still being shaped.