Sr. Security Data Engineer, Vulnerability Risk Management

Moderna is a pioneering company revolutionizing medicine through mRNA technology. They are seeking a Senior Security Data Engineer for Vulnerability Risk Management to enhance and scale data products that support risk modeling and executive visibility, while ensuring high-quality data processing and operational reporting.

Responsibilities:

• Design, build, and operate reliable ingestion pipelines for asset, vulnerability, finding, remediation, threat intelligence, and business-context data sources
• Build reusable ingestion patterns that can absorb new sources without brittle, one-off logic
• Support batch and incremental processing patterns from raw source capture through normalized, model-ready data products
• Partner with source-system owners to clarify schema changes, source freshness, field semantics, and operational constraints
• Own transformations from raw evidence into normalized security entities such as assets, vulnerabilities, findings, owners, exposure context, risk-reduction actions, and validation states
• Engineer model-ready features that encode asset ownership, exposure, business importance, exploitability, remediation state, and organizational context
• Support risk model outputs and operational reporting datasets used by remediation owners and leadership
• Model ownership and accountability signals across code, artifacts, platforms, infrastructure, and application contexts to reduce ambiguity in remediation routing
• Make pragmatic normalization decisions for messy, heterogeneous data while documenting assumptions, confidence, and tradeoffs
• Adapt vulnerability detection and triage data flows to support increasing AI-driven finding volume without losing ownership, severity, or remediation context
• Build data foundations for scanning workflows across code repositories, build artifacts, deployed platforms, and runtime environments
• Connect vulnerability signals to remediation workflow needs, including owner routing, action state, validation evidence, exception paths, and closure reporting
• Partner with security, engineering, and platform teams to unify how findings are represented from discovery through remediation
• Implement data quality checks for schema validity, completeness, freshness, duplication, drift, and unexpected distribution changes
• Maintain lineage documentation so risk scores, reports, and owner views can be traced back to source inputs
• Build monitoring, alerting, and runbooks for data pipeline reliability and source degradation
• Partner with downstream users to resolve data defects and improve trust in pipeline outputs
• Prepare reliable, pre-aggregated data products for dashboards, operational views, risk reporting, and experimentation
• Collaborate with partners to translate new questions into durable data features
• Improve data contracts, documentation, and self-service access patterns for technical and non-technical stakeholders
• Continuously refine feature hypotheses and data products as the vulnerability risk model and remediation operating model mature

Requirements:

• 5+ years of experience in data engineering, analytics engineering, security data engineering, or related technical disciplines
• Strong hands-on proficiency with Python and SQL in production data pipeline environments
• Experience designing, building, and operating ETL/ELT pipelines across heterogeneous source systems
• Experience with data modeling, schema evolution, incremental processing, and data quality controls
• Ability to work with messy, incomplete, or inconsistent datasets and make pragmatic normalization decisions
• Experience supporting data products from source ingestion through reporting, analytics, model inputs, or decision-support outputs
• Experience designing data models that support ownership routing, triage, workflow state, or operational decision-making
• Strong analytical, troubleshooting, documentation, and communication skills
• Experience with medallion, lakehouse, or data-product architecture patterns
• Experience engineering features for probabilistic, predictive, prioritization, or risk models
• Experience in regulated environments such as biotech, pharmaceutical, healthcare, GxP, SOX, or similar control environments
• Exposure to cybersecurity, vulnerability management, threat intelligence, security operations, or security data platforms
• Exposure to application security, software supply chain security, shift-left scanning, artifact security, platform security, or unified remediation workflows
• Experience building lineage, observability, monitoring, and operational runbooks for critical data pipelines
• Familiarity with orchestration, data catalog, data contract, business intelligence, or operational reporting patterns
• Exposure to AI/Agentic/LLM-assisted coding tools