Sr. Security Data Engineer, Vulnerability Risk Management

The Role:

Joining Moderna offers the unique opportunity to be part of a pioneering team that's revolutionizing medicine through mRNA technology, with a diverse pipeline of development programs across various diseases. 

As an employee, you'll be part of a continually growing organization, working alongside exceptional colleagues and strategic partners worldwide, contributing to global health initiatives. 

Moderna's commitment to advancing the technological frontier of mRNA medicines ensures a challenging and rewarding career experience, with the potential to make a significant impact on patients' lives worldwide. 

We are advancing vulnerability risk management by building an evidence-driven data plane that connects asset, vulnerability, threat, remediation, and business-context signals into prioritized action. This role offers the opportunity to shape how Moderna turns high-volume security data into reliable, model-ready products that support risk modeling, owner action, and executive visibility. 

As AI-driven discovery increases vulnerability volume and ownership sprawl, this role will help adapt detection, triage, and data products so teams can identify accountable owners, prioritize the right work, and lay the groundwork for future shift-left scanning and unified remediation workflows across code, artifacts, and platforms. 

We are seeking a Senior Security Data Engineer, Vulnerability Risk Management to own and scale the data products across the full lifecycle: raw source ingestion, normalized evidence, risk model outputs, and operational reporting. 

This is a high-impact, hands-on role for a self-starter who treats security data as a product, balancing engineering rigor, pragmatic normalization, data quality, and business value. 

Here’s What You’ll Do

Data Pipeline Engineering 

• 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. 

Security Data Modeling & Enrichment 

• 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. 

Detection, Triage & Shift-Left Readiness 

• 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. 

Data Quality, Lineage & Reliability 

• 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